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Veenman, C. J., and M. J. T. Reinders. "The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier." IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 9 (2005): 1417–29. http://dx.doi.org/10.1109/tpami.2005.187.
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Chai, Jing, Hongwei Liu, Bo Chen, and Zheng Bao. "Large margin nearest local mean classifier." Signal Processing 90, no. 1 (2010): 236–48. http://dx.doi.org/10.1016/j.sigpro.2009.06.015.
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Widyadhana, Arya, Cornelius Bagus Purnama Putra, Rarasmaya Indraswari, and Agus Zainal Arifin. "A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier." Jurnal Ilmu Komputer dan Informasi 14, no. 1 (2021): 65–71. http://dx.doi.org/10.21609/jiki.v14i1.959.
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K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance of KNN is disadvantaged by the presence of outliers in small sample size datasets and its performance deteriorates on datasets with class imbalance. We propose a local Bonferroni Mean based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) classifier that assigns class label of a query sample dependent on the nearest local centroid mean vector to better represent the underlying statistic of the dataset. The proposed classifier is robust towards outliers because the Nearest Centroid Neighborhood (NCN) concept also considers spatial distribution and symmetrical placement of the neighbors. Also, the proposed classifier can overcome class domination of its neighbors in datasets with class imbalance because it averages all the centroid vectors from each class to adequately interpret the distribution of the classes. The BM-FKNCN classifier is tested on datasets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and benchmarked with classification results from the KNN, Fuzzy-KNN (FKNN), BM-FKNN and FKNCN classifiers. The experimental results show that the BM-FKNCN achieves the highest overall average classification accuracy of 89.86% compared to the other four classifiers.
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Et. al., M. Shanmuganathan,. "ROBUST K-NEAREST NEIGHBOR CLASSIFIER AND NEAREST MEAN CLASSIFIER BASED INFORMATIVE KNOWLEDGE DISTILLATION FACE RECOGNITION." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 418–24. http://dx.doi.org/10.17762/itii.v9i2.365.
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Human Face identification procedures have made tremendous progress in the last decade. Nevertheless, identifying faces with incomplete impediment is as yet challenging for the present face identifiers, and is very much required within certifiable application programs regarding reconnaissance and protection. Though great examination exertion has been dedicated to creating face de-impediment techniques, the greater part of them can just function admirably under obliged conditions. In this manuscript is proposed a Robust K-NNC (K-Nearest Neighbor Classifier) and NMC(Nearest Mean Classifier ) (RKNNC-NMC) prototype to efficiently reestablish incompletely occluded faces even in nature. This model comprises of two-stream, first introduced to perceive high-resolution faces and goal corrupted appearances with a student stream and a teacher stream, separately. A Teacher stream is signified by a Complex RKNNC-NMC for the sake of high-exactness recognition, and the student stream is signified by an a lot more straightforward RKNNC-NMC for low-unpredictability recognition. Broad examinations on synthetic and real datasets datasets of countenances with impediment plainly show the viability of RKNNC-NMC in eliminating various kinds of impediment in one’s face at different locations. The suggested technique additionally gives better behaviour gain than other de-occlusion strategies in advancing recognition execution through partially-occluded faces.
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Mehta, Sumet, Xiangjun Shen, Jiangping Gou, and Dejiao Niu. "A New Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance." Information 9, no. 9 (2018): 234. http://dx.doi.org/10.3390/info9090234.
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The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification performance is highly influenced by the neighborhood size k and existing outliers. In this paper, we propose a new local mean based k-harmonic nearest centroid neighbor (LMKHNCN) classifier in orderto consider both distance-based proximity, as well as spatial distribution of k neighbors. In our method, firstly the k nearest centroid neighbors in each class are found which are used to find k different local mean vectors, and then employed to compute their harmonic mean distance to the query sample. Lastly, the query sample is assigned to the class with minimum harmonic mean distance. The experimental results based on twenty-six real-world datasets shows that the proposed LMKHNCN classifier achieves lower error rates, particularly in small sample-size situations, and that it is less sensitive to parameter k when compared to therelated four KNN-based classifiers.
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Sergioli, Giuseppe, Enrica Santucci, Luca Didaci, Jarosław A. Miszczak, and Roberto Giuntini. "A quantum-inspired version of the nearest mean classifier." Soft Computing 22, no. 3 (2017): 691–705. http://dx.doi.org/10.1007/s00500-016-2478-2.
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Gou, Jianping, Wenmo Qiu, Zhang Yi, Yong Xu, Qirong Mao, and Yongzhao Zhan. "A Local Mean Representation-based K -Nearest Neighbor Classifier." ACM Transactions on Intelligent Systems and Technology 10, no. 3 (2019): 1–25. http://dx.doi.org/10.1145/3319532.
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Gou, J., Z. Yi, L. Du, and T. Xiong. "A Local Mean-Based k-Nearest Centroid Neighbor Classifier." Computer Journal 55, no. 9 (2012): 1058–71. http://dx.doi.org/10.1093/comjnl/bxr131.
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Gou, Jianping, Hongxing Ma, Weihua Ou, Shaoning Zeng, Yunbo Rao, and Hebiao Yang. "A generalized mean distance-based k-nearest neighbor classifier." Expert Systems with Applications 115 (January 2019): 356–72. http://dx.doi.org/10.1016/j.eswa.2018.08.021.
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Masparudin, Masparudin, Abdullah Abdullah, and Usman Usman. "SISTEM PREDIKSI KUALITAS SANTAN KELAPA MENGGUNAKAN NEAREST MEAN CLASSIFIER (NMC)." SISTEMASI 9, no. 3 (2020): 646. http://dx.doi.org/10.32520/stmsi.v9i3.1015.
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Mobarakeh, Ali Khalili, Sayedmehran Mirsafaie Rizi, Saba Nazari, Jiang Ping Gou, and Bakhtiar Affendi Rosdi. "Finger Vein Recognition Using Local Mean Based K-Nearest Centroid Neighbor Classifier." Advanced Materials Research 628 (December 2012): 427–32. http://dx.doi.org/10.4028/www.scientific.net/amr.628.427.
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One of the newest methods of identification system is finger vein recognition which is a unique and successful way to identify human based on the physical characteristics of finger vein patterns. In this paper, a new type of classifier called Local Mean based K-nearest centroid neighbor (LMKNCN) is applied to classify finger vein patterns. Finally, the significance of the proposed method is proven by comparing the results of LMKNCN classifier with traditionally used K nearest neighbor classifier (KNN). The experimental results indicate that the proposed method in this research confidently merits the performance of the finger vein recognition method, as the gained accuracy using the proposed method is higher than that of the traditionally used method KNN. The maximum obtained accuracy of LMKNCN test with 2040 number of finger vein images is 100% while for KNN is 98.53%.
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Neware, Shubhangi, Kamal Mehta, and A. S. Zadgaonkar. "Finger Knuckle Identification using Principal Component Analysis and Nearest Mean Classifier." International Journal of Computer Applications 70, no. 9 (2013): 18–23. http://dx.doi.org/10.5120/11990-7868.
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Veenman, Cor J., and Annabel Bolck. "A sparse nearest mean classifier for high dimensional multi-class problems." Pattern Recognition Letters 32, no. 6 (2011): 854–59. http://dx.doi.org/10.1016/j.patrec.2011.01.011.
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Shanmuganathan, M., and T. Nalini. "Face Recognition using Nearest Neighbour and Nearest Mean Classification Framework : Empirical Analysis, Conclusions and Future Directions." Journal of Physics: Conference Series 2251, no. 1 (2022): 012010. http://dx.doi.org/10.1088/1742-6596/2251/1/012010.
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Abstract Human Face recognition algorithms have made huge progress in the last decade. In this manuscript, we have presented an approach for the implementation of a face recognition system in a successful manner by varying pose, scale, lighting, and age variation. The different empirical analysis was performed with various datasets for face detection and face identification. Face identification system detects efficiently segments and recognizes face in a cluttered sequence under varying pose, lighting and age variations. From this experimental analysis morphological model outperformed k-NNC, NMC based closest mean classifier and informative knowledge distillation with fairly reasonable accuracy. Three proposed methods on the basis of an efficient way of handling the face recognition problems. The morphological method outperformed well when compared with k-NNC, NMC based closest mean classifier a proposed method, and another innovative method named Informative knowledge Distillation. The morphological method is suitable for large datasets where occlusion, pose variation, age variations, and different expression of images.
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Haider, O. Lawend, M. Muad Anuar, and Hussain Aini. "An Improved Flexible Partial Histogram Bayes Learning Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 975–86. https://doi.org/10.11591/ijeecs.v11.i3.pp975-986.
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This paper presents a proposed supervised classification technique namely flexible partial histogram Bayes (fPHBayes) learning algorithm. The traditional classification algorithms like neural network, support vector machine, first nearest neighbor, nearest subclass classifier and Gaussian mixture model classifier are accurate but slow when dealing with large number of instances. In additional to that these algorithms might require to be retrain when the classes changes. On the other hand, algorithms like naïve Bayes and nearest class mean are fast but not accurate. It is difficult and challenging to have a classification algorithm that is fast and accurate when dealing with large number of instances. In our previous work, partial histogram Bayes (PHBayes) learning algorithm showed some advantages in the aspects of speed and accuracy in classification tasks. However, its accuracy declines when dealing with small number of instances or when the class feature distributes in wide area. In this work, the proposed fPHBayes solves these limitations. fPHBayes is able to work fast with good accuracy with large and small number of instances. fPHBayes uses a probability distribution derived from smoothing the observed histogram in order to represent the class. Then it performs the classification using the Bayesian rule. fPHBayes was analyzed and compared with PHBayes and other standard learning algorithms like first nearest neighbor, nearest subclass mean, nearest class mean, naive Bayes and Gaussian mixture model classifier. The experiments were performed using both real data and synthetic data considering different number of instances and different variances of Gaussians. The results showed that fPHBayes is more accurate and flexible to deal with different number of instances and different variances of Gaussians as compared to other classifiers.
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GIL-PITA, ROBERTO, and XIN YAO. "EVOLVING EDITED k-NEAREST NEIGHBOR CLASSIFIERS." International Journal of Neural Systems 18, no. 06 (2008): 459–67. http://dx.doi.org/10.1142/s0129065708001725.
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The k-nearest neighbor method is a classifier based on the evaluation of the distances to each pattern in the training set. The edited version of this method consists of the application of this classifier with a subset of the complete training set in which some of the training patterns are excluded, in order to reduce the classification error rate. In recent works, genetic algorithms have been successfully applied to determine which patterns must be included in the edited subset. In this paper we propose a novel implementation of a genetic algorithm for designing edited k-nearest neighbor classifiers. It includes the definition of a novel mean square error based fitness function, a novel clustered crossover technique, and the proposal of a fast smart mutation scheme. In order to evaluate the performance of the proposed method, results using the breast cancer database, the diabetes database and the letter recognition database from the UCI machine learning benchmark repository have been included. Both error rate and computational cost have been considered in the analysis. Obtained results show the improvement achieved by the proposed editing method.
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Azhari, Bayu Rianto ,. "SISTEM CERDAS MENDETEKSI KUALITAS GULA MERAH MENGGUNAKAN METODE NEAREST MEAN CLASSIFIER (NMC)." Selodang Mayang: Jurnal Ilmiah Badan Perencanaan Pembangunan Daerah Kabupaten Indragiri Hilir 5, no. 3 (2019): 149. http://dx.doi.org/10.47521/selodangmayang.v5i3.133.
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The development and utilization of digital images has developed rapidly. At present, digital image processing capabilities and techniques make it possible to be used more effectively and efficiently in identifying quality classes of brown sugar. One of them is the concept of Smart Systems with the use of Matlap-based applications so that public recognition of the importance of selecting good quality brown sugar can be a little more efficient. Digital image processing capabilities are supported by the concept of pattern recognition and classification, it is expected that the quality classification of brown sugar based on RGB color variables (Red, Green, Blue) and texture variables (energy, contrast, correlation and homogeneity) with the help of computers can be realized. To get a solution of the problem of classification and determine the accuracy of the classification of the quality of brown sugar into a certain class, then we need a method that is able to classify the quality of brown sugar brown sugar into class A (very good), class B (good) and class C (not good ). The method is expected to also be able to handle the problem of the accuracy of the classification of brown sugar into certain quality classes according to the actual state of brown sugar.
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Mailagaha Kumbure, Mahinda, Pasi Luukka, and Mikael Collan. "A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean." Pattern Recognition Letters 140 (December 2020): 172–78. http://dx.doi.org/10.1016/j.patrec.2020.10.005.
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Kekre, Dr H. B., Dr Tanuja K. Sarode, and Jagruti K. Save. "An Efficient Method for Similarity Measure in Independent PCA based Classification." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 3 (2013): 1099–109. http://dx.doi.org/10.24297/ijct.v9i3.3335.
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The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the feature vectors of training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest training feature vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of feature vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test feature vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared with distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of training set.
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B.Meena, Preethi, and P.Radha. "Disease Classification and Prediction using Ensemble MachineLearning Classification Algorithm." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 6 (2021): 202–14. https://doi.org/10.5281/zenodo.8020640.
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<strong>Abstract:</strong> In today’s scenario, disease prediction plays an important role in medical field. Early detection of diseases is essential because of the fast food habits and life. In my previous study for predicting diseases using radiology test report , and to classify the disease as positive or negative three classifiers Naïve Bayes (NB), Support Vector Machine (SVM) and Modified Extreme Learning Machine (MELM was used to increase the accuracy of results. To increase the efficiency of predicting the disease and to find which disease pricks the society, ensemble machine learning algorithm is used. The huge data from the healthcare industry were preprocessed., categorized and analyzed to find out and predict which patient to be treated and given priority and which hits the society the most. Ensemble machine learning's popularity in the medical industry is due to a variety of factors the Classifiers used are K Nearest Neighbors, Nearest Mean Classifier, Mean Feature Voting Classifier, KDtree KNN, Random Forest. To reduce the manual processes in medical field automating these processes has become important. Electronic medical records and significant advances in health care have given an opportunity to make find out which patients need to be given more importance. Several methodologies and techniques were used to preprocess the data in order to meet the study' requirements. To improve the performance of machine learning algorithms, feature selections were made using Tabu search. When ensemble prediction is combined with the Random Forest algorithm as the combiner, the results are more reliable. The aim of this study is to create a system to classify Medical records whether it is diseased or not and find out which disease rate has increased. This research will help the society to an individual to get treated easily and take preventive measures to avoid diseases.
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Firmansyah, Andika, Abdullah Abdullah, and Samsudin Samsudin. "Rancang Bangun Sistem Klasifikasi Biji Pinang Menggunakan Metode Nearest Mean Classifier Berbasis Android." SISTEMASI 10, no. 1 (2021): 250. http://dx.doi.org/10.32520/stmsi.v10i1.1207.
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Junoh, Ahmad Kadri, Muhammad Naufal Mansor, Alezar Mat Ya’acob, Siti Haida Ismail, and Nurhidayah Omar. "Bandit Detection System under Various Noise Levels with Nearest Mean and Gaussian Classifier." Advanced Materials Research 816-817 (September 2013): 540–44. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.540.
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The Government Transformation Programme (GTP) is an effort by Malaysia's current Government to address seven key areas concerning the people of the country. The programme was unveiled on 28 January 2010 by the Malaysian Prime Minister Najib Tun Razak. One of the (GTP) agenda is to reduce the crime rate as per its Vision 2020. Thus, in order fulfill this demand and challenge our enthusiasm to create a better place for our beloved country. We proudly presented a bandit detection system under various noise levels with nearest mean and Gaussian classifier. This system to boost the Malaysian police arm forced performance.
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Mandar, Gamaria, Abdul Haris Muhammad, M. Santosa, and Salsabila Salsabila. "KLASIFIKASI KUALITAS KOPRA MENGGUNAKAN NEAREST MEAN CLASSIFIER BERDASARKAN WARNA DAN TEKSTUR LOCAL BINARY PATTERN." IJIS - Indonesian Journal On Information System 8, no. 2 (2023): 164. http://dx.doi.org/10.36549/ijis.v8i2.251.
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Kopra adalah daging kelapa yang telah dikeringkan dengan menggunakan cara tradisional maupun modern. Umum para petani menggunakan cara tradisional seperti dikeringkan dengan memanfaatkan cahaya matahari dan bara api (pengasapan), dimana panas suhu diantaranya 40-80 derajat celcius untuk menghasilkan kopra dengan kualitas yang baik. Kopra yang merupakan bahan baku minyak kelapa ini tidak bisa terlepas dari kebutuhan masyarakat sehingga kualitas kopra sangat wajib diperhatikan oleh seorang petani atau pembeli. Umumnya untuk mengetahui kualitas kopra dilakukan secara langsung dengan memanfaatkan penglihatan, penciuman dan menyentuh bahan baku tersebut dan mencocokan dengan parameter-parameter kualitas kopra yang baik. Sehingga pada penelitian ini penulis menerapkan Algoritma Nearest Mean Classifier dengan memanfaatkan citra atau gambar kopra sebanyak 100 sampel yang diambil menggunakan kamera standar untuk klasifikasikan kualitas kopra dengan memanfaatkan warna dan tekstur local binary pattern pada citra kopra. Penelitian ini menggunakan matlab untuk pengolah data dan membangun interface aplikasi. Hasil dari penelitian ini menujukan bahwa dari 20 citra yang diujikan hanya 2 data uji gagal, sehingga hasil akurasi mencapai 90%.Kata Kunci: Klasifikasi, Kopra, Nearest Mean Classifier
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Rajkumar Palaniappan, Kenneth Sundaraj, Sebastian Sundaraj, N. Huliraj, S. S. Revadi, and B. Archana. "Pulmonary Acoustic Signal Classification Using Autoregressive Coefficients and k-Nearest Neighbor." Applied Mechanics and Materials 591 (July 2014): 211–14. http://dx.doi.org/10.4028/www.scientific.net/amm.591.211.
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— Pulmonary acoustic signals provide important information of the condition of the respiratory system. It can be used to assist medical professionals as an alternative diagnosis tool. In this paper, we intend to discriminate between normal (without any pathological condition), Airway Obstruction (AO) pathology and Interstitial lung disease (ILD) pathology using pulmonary acoustic signals. The proposed method filters the heart sounds and other artifacts using a butterworth bandpass filter and windowed to 256 samples per segment. The autoregressive coefficients (AR coefficients) were extracted as features from the pulmonary acoustic signals. The extracted features are distinguished using k-nearest neighbor (k-nn) classifier. The classifier performance is analysed by using confusion matrix technique. A mean classification accuracy of 96.12% was reported for the proposed method. The performance analysis of the knn classifier using confusion matrix revealed that normal, AO and ILD pathology are classified at 94.36%, 95.18% and 94.68% classification accuracy respectively. The analysis reveals that the proposed method performs better in distinguishing between the normal, AO and ILD.Keywords—Respiratorysound,ARcoefficients,k-nearestneighbor,confusionmatrix
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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Calcium Channel Parameters With KNN And ANN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 919–25. http://dx.doi.org/10.18137/cardiometry.2022.25.919925.
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Aim: Aim of this research is to analyze and compare ventricular cardiac arrhythmia classification using calcium channel parameters with Artificial Neural Network (ANN) and K- Nearest Neighbour (KNN) classifier. Materials and Methods: For the classification of arrhythmias, A.V.Panifilov (AVP) is used. THVCM contains well defined Calcium channel dynamics and its properties. Sample size was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20 for each analysis and will be imported to the classifier such as K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) classifiers to find better accuracy. Finally, the results (accuracy) will be validated by using Statistical Package for the Social Science (SPSS) software. Results: The results obtained from Normal, Tachycardia and Bradycardia data are imported to the ANN and KNN classifier. In which KNN shows accuracy value (12.3950%), standard deviation (0.96490) and Standard error mean (0.21576). And ANN shows accuracy value (35.3400%), standard deviation (3.22285) and Standard error mean (0.72065). Conclusion: From the results, it is concluded that ANN produces better results when compared with KNN classification in terms of accuracy.
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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Sodium Channel Parameters With ANN And KNN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 911–18. http://dx.doi.org/10.18137/cardiometry.2022.25.911918.
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Aim: Aim of this research is to analyze and compare ventricular Cardiac Arrhythmia (CA) classification using Sodium Channel (Na+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbour (KNN) classifiers. Materials and Methods: Ten Tusscher Human Ventricular Cell Model (THVCM) (data) is used for arrhythmias classification. THVCM has well defined sodium (Na+) channel dynamics. Sample size was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20 for each analysis and will be imported to the classifier, K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) classifier to find better accuracy. Finally, the results (accuracy) will be validated by using Statistical Package for the Social Science (SPSS) software. Result: Ventricular normal, tachycardia and bradycardia data are fed into novel ANN and KNN classifiers. The results obtained from classifiers for 20 samples are fed to SPSS. In that ANN shows accuracy of 35.6% with standard deviation (3.17822) and Standard error mean (0.71067). Similarly KNN produces an accuracy value of 18.05% with standard deviation (1.19593) and Standard error mean (0.26739). Conclusion: As per the results, it clearly shows that the novel ANN has better accuracy for classification than KNN.
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Azeez, N. A., S. S. Oladele, and O. Ologe. "Identification of pharming in communication networks using ensemble learning." Nigerian Journal of Technological Development 19, no. 2 (2022): 172–80. http://dx.doi.org/10.4314/njtd.v19i2.10.
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Pharming scams are carried out by exploiting the DNS as the main weapon while phishing attacks employ spoofed websites that appear to be legitimate to internet users. Phishing makes use of baits such as fake links but pharming leverages and negotiates on the DNS server to move and redirect internet users to a fake and simulated website.Having seen several challenges through pharming resulting into vulnerable websites, personal emails and accounts on social media, the usage and reliability on internet calls for caution. Against this backdrop, this work aims at enhancing pharming detection strategies by adopting machine learning classification algorithms. To further obtain the best classification results, an ensemble learning approach was adopted. The algorithms used include K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gaussian Naive Bayes, Logistic Regression, Support Vector Machine, Adaptive Boosting, Gradient Boosting, and Extra Trees Classifier. During the testing process, the classifiers were tested against four popular metrics: accuracy, recall, precision, F1 score, and Log loss. The results demonstrate the performance of all algorithms used, as well as their relationships. The ensemble model that included Logistic Regression, K-Nearest Neighbors, Decision Tree, Support Vector Machine, Gradient Boosting Classifier, AdaBoost Classifier, Extra Trees Classifier, and Random Forest produced the best results after evaluating them on the two datasets. Random Forest Classifiers showed a better performance of the classifiers, with mean accuracies of 0.932 and 0.939, respectively for each of the datasets when compared to 0.476 and 0.519 obtained for Naive Bayes.
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Ridha, Muh Rasyid, and Fitri Yunita. "Pemilihan Bibit Kelapa Menggunakan Metode Nearest Mean Classifier Untuk Masyarakat Petani Kelapa Di Kabupaten Indragiri Hilir." JURNAL PERANGKAT LUNAK 2, no. 3 (2020): 101–14. http://dx.doi.org/10.32520/jupel.v2i3.1306.
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Kelapa yang merupakan tanaman tahunan yang baru bisa berproduksi setelah berumum lebih kurang lima tahun, oleh karenanya harus dibuat rencana yang matang terkait pemilihan bahan tanam atau bibit kelapa untuk menghindari kerugian baik materiil ataupun waktu. Kesalahan dalam pemilihan bibit dapat menjadikan tanaman kelapa tidak mampu berproduksi secara optimal, walaupun sudah diperlakukan dengan standar operasional teknis budidaya yang tepat. Target khusus dalam penelitian ini yaitu menyusun sistem klasifikasi bibit kelapa menggunakan Nearest Mean Classifier (NMC) Method, klasifikasi bibit kelapa dibentuk kedalam kelas grade A, grade B dan, grade C. Hasil klasifkasi sistem 10 x 10 – fold crossvalidation pada masing-masing katagori kualitas bibit kelapa yaitu: Grade A (80-85 %) dihasilkan ketelitian rata-rata 56,791 % dengan simpangan baku 12,05715 %. Grade B (70-75 %) rata-rata dihasilkan ketelitian 52,525 % dengan simpangan baku 7,481074 %. Grade C (60-65 %) rata-rata dihasilkan ketelitian 62,002 % dengan simpangan baku 16,36763 %. Dapat dinyatakan bahwa Grade C yang memiliki persentase tertinggi dengan jarak 0,379% dan prosentase kemiripan 62 %. Hasil akhir evaluasi dari data eksperimen secara global, memiliki ketelitian rata-rata 86.67 %. Untuk mendapatkan persentase kemiripan yang tinggi harus dilakukan data latih yang banyak terhadap sistem klasifikasi kualitas bibit kelapa.
 
 Kata Kunci – Bibit Kelapa, Nearest Mean Classifier, Indragiri Hilir
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Abdullah, Abdullah, Agus Harjoko, and Othman Mahmod. "Classification of Fruits Based on Shape and Color using Combined Nearest Mean Classifiers." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 1 (2023): 51–57. http://dx.doi.org/10.29207/resti.v7i1.4693.
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Fruit classification is an important task in many agriculture industry. The fruit classification system can be used to identify the types and prices of fruit. Manual classification of fruit is not efficient for large amount of fruits. The advancement of information technology has made possible fruit classification be done by a machine. This research aims to propose a fruit classification methodology based on shape and color. To reduce the effect of lighting variability a color normalization is carried out prior to feature extraction. The color features used in this research are mean and standard deviation. The shape features are area, perimeter, and compactness. The classification of an unknown fruit is carried out using the nearest mean classifier. The method developed in this research is tested using 12 classes of fruits where each class is represented by a number of samples. The experimental results show that the method proposed in this research provides an accuracy of 95.83% for two samples per class and 100% for three samples per class. Experiment on small training samples has been conducted to evaluate the performance of the proposed combined nearest mean classifiers and results obtained showed that the technique was able to provide good accuracy.
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Du, Hongle, Yan Zhang, Lin Zhang, and Yeh-Cheng Chen. "Selective ensemble learning algorithm for imbalanced dataset." Computer Science and Information Systems, no. 00 (2023): 23. http://dx.doi.org/10.2298/csis220817023d.
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Under the imbalanced dataset, the performance of the base-classifier, the computing method of weight of base-classifier and the selection method of the base-classifier have a great impact on the performance of the ensemble classifier. In order to solve above problem to improve the generalization performance of ensemble classifier, a selective ensemble learning algorithm based on under-sampling for imbalanced dataset is proposed. First, the proposed algorithm calculates the number K of under-sampling samples according to the relationship between class sample density. Then, we use the improved K-means clustering algorithm to under-sample the majority class samples and obtain K cluster centers. Then, all cluster centers (or the sample of the nearest cluster center) are regarded as new majority samples to construct a new balanced training subset combine with the minority class?s samples. Repeat those processes to generate multiple training subsets and get multiple base-classifiers. However, with the increasing of iterations, the number of base-classifiers increase, and the similarity among the base-classifiers will also increase. Therefore, it is necessary to select some base-classifier with good classification performance and large difference for ensemble. In the stage of selecting base-classifiers, according to the difference and performance of base-classifiers, we use the idea of maximum correlation and minimum redundancy to select base-classifiers. In the ensemble stage, G-mean or F-mean is selected to evaluate the classification performance of base-classifier for imbalanced dataset. That is to say, it is selected to compute the weight of each base-classifier. And then the weighted voting method is used for ensemble. Finally, the simulation results on the artificial dataset, UCI dataset and KDDCUP dataset show that the algorithm has good generalization performance on imbalanced dataset, especially on the dataset with high imbalance degree.
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Ćwiklińska-Jurkowska, Małgorzata. "Gene selection ensembles and classifier ensembles for medical diagnosis." Biometrical Letters 56, no. 2 (2019): 117–38. http://dx.doi.org/10.2478/bile-2019-0007.
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SummaryThe usefulness of combining methods is examined using the example of microarray cancer data sets, where expression levels of huge numbers of genes are reported. Problems of discrimination into two groups are examined on three data sets relating to the expression of huge numbers of genes. For the three examined microarray data sets, the cross-validation errors evaluated on the remaining half of the whole data set, not used earlier for the selection of genes, were used as measures of classifier performance. Common single procedures for the selection of genes—Prediction Analysis of Microarrays (PAM) and Significance Analysis of Microarrays (SAM)—were compared with the fusion of eight selection procedures, or of a smaller subset of five of them, excluding SAM or PAM. Merging five or eight selection methods gave similar results. Based on the misclassification rates for the three examined microarray data sets, for any examined ensemble of classifiers, the combining of gene selection methods was not superior to single PAM or SAM selection for two of the examined data sets. Additionally, the procedure of heterogeneous combining of five base classifiers—k-nearest neighbors, SVM linear and SVM radial with parameter c=1, shrunken centroids regularized classifier (SCRDA) and nearest mean classifier—proved to significantly outperform resampling classifiers such as bagging decision trees. Heterogeneously combined classifiers also outperformed double bagging for some ranges of gene numbers and data sets, but merging is generally not superior to random forests. The preliminary step of combining gene rankings was generally not essential for the performance for either heterogeneously or homogeneously combined classifiers.
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Mahmud, Mat Nizam, Mohammad Nizam Ibrahim, Muhammad Khusairi Osman, and Zakaria Hussain. "Fault Classification in Transmission Line Using Wavelet Features and Fuzzy-KNN." Applied Mechanics and Materials 850 (August 2016): 112–17. http://dx.doi.org/10.4028/www.scientific.net/amm.850.112.
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Fault occurred in transmission line can cause many problems such as failure of equipment, instability in power flow, and economical losses. Many of the techniques proposed for fault classification in transmission line have applied steady state component as it is easily affected by the surroundings. Then, protection scheme based on fault generated transient that can offer an accurate result for fault classification in power system should be proposed. This paper presents the fault classification scheme using Fuzzy-KNN (Fuzzy k-Nearest Neighbor) classifier and wavelet features. Two wavelet features were calculated in this study which are Wavelet Mean (μ) and Wavelet Standard Deviation (σ). Then, the Fuzzy-KNN classifier was tested with three datasets categories: Ideal, 30 dB noise, and 20 dB noise datasets. The overall results in accuracy performance show that the Fuzzy-KNN classifier performed better than the KNN (k-Nearest Neighbor) classifier.
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Hidayatullah, Adam Syarif, Fitra Abdurrachman Bachtiar, and Imam Cholissodin. "Penerapan Algoritme Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance (LMKHNCN) Untuk Klasifikasi Hasil Kinerja Pegawai Negeri Sipil." Jurnal Teknologi Informasi dan Ilmu Komputer 8, no. 6 (2021): 1287. http://dx.doi.org/10.25126/jtiik.2021834431.
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<p class="Abstrak">Keberhasilan sebuah perusahaan terjadi karena dapat mengelola sumber daya manusianya dengan baik begitu juga sebaliknya. Salah satu instansi yang mengelola sumber daya manusia menggunakan Manajemen Talenta adalah Badan Kepegawaian Daerah (BKD) kota Malang, dengan mengevaluasi pegawainya setiap tahunnya setelah pekerjaan selesai dilakukan. Hal ini menyebabkan hasil pekerjaan yang telah dilakukan tidak optimal, sehingga perlu identifikasi dini pegawai yang memiliki kinerja dibawah rata – rata sehingga dapat dievaluasi dan meminimalisir hasil pekerjaan yang tidak optimal dengan menggunakan teknik klasifikasi. Penelitian ini menggunakan teknik klasifikasi <em>Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance</em> (LMKHNCN). Metode ini merupakan metode modifikasi dari metode <em>K-Nearest Neighbor</em> (KNN) dan dibuktikan memiliki performa lebih baik dibandingkan dengan metode aslinya KNN. Dilakukan pengujian <em>F1-Score</em> dan akurasi menggunakan <em>K-Fold Cross Validation</em> untuk mengetahui persebaran akurasi dan juga pengujian mengenai pengaruh normalisasi karena tidak ada informasi normalisasi pada penelitian sebelumnya. Metode pada kasus ini menghasilkan performa klasifikasi yang baik, dibuktikan bahwa hasil akurasi dan <em>F1-Score</em> oleh metode ini berturut – turut ialah mencapai 98,8% dan 98,1%.</p><p class="Abstrak"> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p><em>The success of company occurs because is manage human resources well and vice versa. One of institute that mange human resource using Talent Management is Malang city Badan Kepegawaian Daerah (BKD), which evaluates its employee annually after the work is completed. This can cause not optimal work result, so it necessary to early identification of employees who have performance below average performance so that can be evaluated and minimize not optimal result. This study is use classification technique Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance (LMKHNCN). This method is modified base algorithm of K-Nearest Neighbor (KNN). F1-Score and Accuracy using K-Fold Cross Validation to measure performance of this method and normalization testing due to no any information about that in previous study. This method is proven to have better performance compared to it original algorithm KNN. The method in this study has produced good classification performance. The result of classification accuracy and F1-Score by this method reach </em><em>98,8% dan 98,1%</em>.</p>
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Muni, Abdul, and Muhammad Amin. "DETEKSI DAN KLASIFIKASI HAMA DAN PENYAKIT TANAMAN KELAPA MENGGUNAKAN NEAREST MEAN CLASSIFIER DI KABUPATEN INDRAGIRI HILIR." JURNAL PERANGKAT LUNAK 6, no. 3 (2024): 414–27. http://dx.doi.org/10.32520/jupel.v6i3.3655.
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Tanaman kelapa di Kabupaten Indragiri Hilir menghadapi masalah serius akibat serangan hama dan penyakit (OPT) yang mengakibatkan penurunan produktivitas dan kerugian ekonomi bagi petani. Kendala utama dalam penanganan OPT adalah sulitnya melakukan deteksi dan klasifikasi secara cepat dan akurat. Untuk mengatasi masalah ini, penelitian ini mengembangkan sistem klasifikasi OPT pada tanaman kelapa menggunakan metode Nearest Mean Classifier (NMC). Metode ini menggunakan citra digital tanaman kelapa untuk menghitung kemiripan antara citra uji dan data latih berdasarkan jarak Euclidean. Hasil penelitian menunjukkan bahwa sistem yang dibangun mampu mengklasifikasikan OPT dengan akurasi tinggi. Pada kelas Bercak Daun, rata-rata jarak yang dihasilkan adalah 75,54 dengan simpangan baku 24,74, sedangkan pada kelas Akar Jatuh, rata-rata jaraknya adalah 22,97 dengan simpangan baku 6,20. Secara keseluruhan, sistem menunjukkan persentase akurasi tertinggi sebesar 89,87% untuk kelas Bercak Daun dan 14,12% untuk kelas Busuk Daun. Sistem ini memberikan solusi yang efisien bagi petani dalam mendeteksi OPT dan diharapkan dapat meningkatkan produktivitas kelapa di Kabupaten Indragiri Hilir.
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Glowacz, A., A. Glowacz, and P. Korohoda. "Recognition of Monochrome Thermal Images of Synchronous Motor with the Application of Binarization and Nearest Mean Classifier." Archives of Metallurgy and Materials 59, no. 1 (2014): 31–34. http://dx.doi.org/10.2478/amm-2014-0005.
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Abstract This article discusses the recognition method of imminent failure conditions of synchronous motor. The proposed approach is based on a study of thermal images of the motor. Studies were carried out for four conditions of motor with the application of binarization and nearest mean classifier with Manhattan distance. Pattern creation process used 40 monochrome thermal images. Identification process was carried out for 160 monochrome thermal images. The experiments show that the method can be useful for protection of synchronous motor. Moreover, this method can be used to diagnose equipments in steelworks and other industrial plants.
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Ramos-Martinez, Moises, Felipe D. J. Sorcia-Vázquez, Gerardo Ortiz-Torres, et al. "Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques." Algorithms 17, no. 11 (2024): 527. http://dx.doi.org/10.3390/a17110527.
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Sleep apnea is a sleep disorder that disrupts breathing during sleep. This study aims to classify sleep apnea using a machine learning approach and a Euler–Poincaré characteristic (EPC) model derived from electrocardiogram (ECG) signals. An ensemble K-nearest neighbors classifier and a feedforward neural network were implemented using the EPC model as inputs. ECG signals were preprocessed with a polynomial-based scheme to reduce noise, and the processed signals were transformed into a non-Gaussian physiological random field (NGPRF) for EPC model extraction from excursion sets. The classifiers were then applied to the EPC model inputs. Using the Apnea-ECG dataset, the proposed method achieved an accuracy of 98.5%, sensitivity of 94.5%, and specificity of 100%. Combining machine learning methods and geometrical features can effectively diagnose sleep apnea from single-lead ECG signals. The EPC model enhances clinical decision-making for evaluating this disease.
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Vanita, Rani*1 &. Er. Gurjit Singh Bhathal2. "DETECTION AND CLASSIFICATION OF CARDIAC ARRHYTHMIAS USING ECG BIG DATA ANALYSIS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 7 (2018): 72–82. https://doi.org/10.5281/zenodo.1305020.
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In this work, we have presented a feature extraction method in order to differentiate normal ECG signal from arrhythmias LBBB and RBBB ECG pulses. The method first detect the high amplitude R peaks from the signal, Then interval between two R signals is used to locate other peaks named as P, Q, S and T. After that five different types of time domain features has been extracted which are named as mean NN, SDNN, SDSD, RMSSD and pNN50 from samples of 22 patients. For classification, training and testing of features has been carried out using K-nearest neighbor and decision tree classifiers. Experimental results shows that proposed system effectively classifies the normal, LBBB and RBBB ECG pulses into corresponding classes out of which decision tree gives 95% accuracy in classification whereas k-NN classifier gives 91% accuracy in classification.
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Sergioli, Giuseppe, Giorgio Russo, Enrica Santucci, et al. "Quantum-inspired minimum distance classification in a biomedical context." International Journal of Quantum Information 16, no. 08 (2018): 1840011. http://dx.doi.org/10.1142/s0219749918400117.
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We propose an application of a quantum-inspired version of the Nearest Mean Classifier (NMC) (G. Sergioli, E. Santucci, L. Didaci, J. A. Miszczak and R. Giuntini, A quantum inspired version of the NMC classifier, Soft Comput. 22(3) (2018) 691. G. Sergioli, G. M. Bosyk, E. Santucci and R. Giuntini, A quantum-inspired version of the classification problem, Int. J. Theo. Phys. 56(12) (2017) 3880. E. Santucci and G. Sergioli, Classification problem in a quantum framework, in quantum foundations, probability and information, Proc. Quantum and Beyond Conf., 13–16 June 2016, Vaxjo, Sweden, A. Khrennikov and T. Bourama, Springer-Berlin, Germany, 2018 (in press, 2018). E. Santucci, Quantum minimum distance classifier, Entropy 19(12) (2017) 659.) to a biomedical context. In particular, we benchmark the performances of such a quantum-variant of NMC against NMC and other (nonlinear) classifiers with respect to the problem of classifying the probability of survival for patients affected by idiopathic pulmonary fibrosis (IPF).
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Zararsiz, Gokmen, Dincer Goksuluk, Bernd Klaus, et al. "voomDDA: discovery of diagnostic biomarkers and classification of RNA-seq data." PeerJ 5 (October 6, 2017): e3890. http://dx.doi.org/10.7717/peerj.3890.
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RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom’s precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poisson linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.
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Chang, Mahmud, Shin, Nguyen-Quang, Price, and Prithiviraj. "Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection." AgriEngineering 1, no. 3 (2019): 434–52. http://dx.doi.org/10.3390/agriengineering1030032.
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Strawberry is an important fruit crop in Canada but powdery mildew (PM) results in about 30–70% yield loss. Detection of PM through an image texture-based system is beneficial, as it identifies the symptoms at an earlier stage and reduces labour intensive manual monitoring of crop fields. This paper presents an image texture-based disease detection algorithm using supervised classifiers. Three sites were selected to collect the leaf image data in Great Village, Nova Scotia, Canada. Images were taken under an artificial cloud condition with a Digital Single Lens Reflex (DSLR) camera as red-green-blue (RGB) raw data throughout 2017–2018 summer. Three supervised classifiers, including artificial neural networks (ANN), support vector machine (SVM), and k-nearest neighbors (kNN) were evaluated for disease detection. A total of 40 textural features were extracted using a colour co-occurrence matrix (CCM). The collected feature data were normalized, then used for training and internal, external and cross-validations of developed classifiers. Results of this study revealed that the highest overall classification accuracy was 93.81% using the ANN classifier and lowest overall accuracy was 78.80% using the kNN classifier. Results identified the ANN classifier disease detection having a lower Root Mean Square Error (RMSE) = 0.004 and Mean Absolute Error (MAE) = 0.003 values with 99.99% of accuracy during internal validation and 87.41%, 88.95% and 95.04% of accuracies during external validations with three different fields. Overall results demonstrated that an image texture-based ANN classifier was able to classify PM disease more accurately at early stages of disease development.
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Du, Peng, Xiao Liu, Xuefan Wu, Jiawei Chen, Aihong Cao, and Daoying Geng. "Predicting Histopathological Grading of Adult Gliomas Based On Preoperative Conventional Multimodal MRI Radiomics: A Machine Learning Model." Brain Sciences 13, no. 6 (2023): 912. http://dx.doi.org/10.3390/brainsci13060912.
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Purpose: The accurate preoperative histopathological grade diagnosis of adult gliomas is of great significance for the formulation of a surgical plan and the implementation of a subsequent treatment. The aim of this study is to establish a predictive model for classifying adult gliomas into grades 2–4 based on preoperative conventional multimodal MRI radiomics. Patients and Methods: Patients with pathologically confirmed gliomas at Huashan Hospital, Fudan University, between February 2017 and July 2019 were retrospectively analyzed. Two regions of interest (ROIs), called the maximum anomaly region (ROI1) and the tumor region (ROI2), were delineated on the patients’ preoperative MRIs utilizing the tool ITK-SNAP, and Pyradiomics 3.0 was applied to execute feature extraction. Feature selection was performed utilizing a least absolute shrinkage and selection operator (LASSO) filter. Six classifiers, including Gaussian naive Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) with a linear kernel, adaptive boosting (AB), and multilayer perceptron (MLP) were used to establish predictive models, and the predictive performance of the six classifiers was evaluated through five-fold cross-validation. The performance of the predictive models was evaluated using the AUC and other metrics. After that, the model with the best predictive performance was tested using the external data from The Cancer Imaging Archive (TCIA). Results: According to the inclusion and exclusion criteria, 240 patients with gliomas were identified for inclusion in the study, including 106 grade 2, 68 grade 3, and 66 grade 4 gliomas. A total of 150 features was selected, and the MLP classifier had the best predictive performance among the six classifiers based on T2-FLAIR (mean AUC of 0.80 ± 0.07). The SVM classifier had the best predictive performance among the six classifiers based on DWI (mean AUC of 0.84 ± 0.05); the SVM classifier had the best predictive performance among the six classifiers based on CE-T1WI (mean AUC of 0.85 ± 0.06). Among the six classifiers, based on ROI1, the MLP classifier had the best prediction performance (mean AUC of 0.78 ± 0.07); among the six classifiers, based on ROI2, the SVM classifier had the best prediction performance (mean AUC of 0.82 ± 0.07). Among the six classifiers, based on the multimodal MRI of all the ROIs, the SVM classifier had the best prediction performance (average AUC of 0.85 ± 0.04). The SVM classifier, based on the multimodal MRI of all the ROIs, achieved an AUC of 0.81 using the external data from TCIA. Conclusions: The prediction model, based on preoperative conventional multimodal MRI radiomics, established in this study can conveniently, accurately, and noninvasively classify adult gliomas into grades 2–4, providing certain assistance for the precise diagnosis and treatment of patients and optimizing their clinical management.
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Ghazi Darweesh, Alya, and Mofeed Turky Rashid. "Design of Hand Gesture Classification System Based on High Density-Surface Electromyography Accompanied Force Myography." Iraqi Journal for Electrical and Electronic Engineering 21, no. 2 (2025): 265–83. https://doi.org/10.37917/ijeee.21.2.24.
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A robust system that classifies various hand gestures would greatly help those using prosthetic limbs. Recently, emphasis has been placed on extracted features from the High Density - surface Electromyography (HD-sEMG) signals and the size of segmentation windows which augment the recognition accuracy. This paper proposes a hand gestures identification system, in which HD-sEMG signals are employed, and is supported by Force Myography (FMG) signals for this mission. Several feature types have been extracted from FMG and HD-sEMG signals such as MEAN, RMS, MAD, STD, and Variance, these features have been validated under some classifiers such as decision tree (DT), linear discriminant analysis (LDA), support vector machine SVM, and k-nearest neighbor (KNN), in which results showing that MEAN and RMS features are superior to others, while the best classifier is SVM. Several experiments have been achieved by the MATLAB platform to validate the proposed system, in which, a database of HD-sEMG signals comprising 65 isometric hand gestures is employed, where two (8×8) electrodes and 9 force sensors are used to collect the FMG data. This data was derived from 20 intact participants, the first preprocessing step was applied during the recording stage. Ten gestures are chosen to be classified from the 65 hand gestures. Results show the success of the proposed system while the classification accuracy arrived at 99.1%.
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Saraswati, Mei Sita, Moch Abdul Mukid, and Abdul Hoyyi. "METODE GENERALIZED MEAN DISTANCE-BASED K-NEAREST NEIGHBOR CLASSIFIER (GMDKNN) UNTUK ANALISIS CREDIT SCORING CALON DEBITUR KREDIT TANPA AGUNAN (KTA)." Jurnal Gaussian 8, no. 1 (2019): 149–60. http://dx.doi.org/10.14710/j.gauss.v8i1.26629.
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Unsecured Credit is one of the credit facilities provided by banks, where the prospective debtor can borrow some amount of fund from the bank without having to provide collateral. Credit scoring is a process that aims to assess the worthiness of credit applications and classify the credit applicants into prospective debtors whose the credit application is worthy to be accepted and prospective debtors whose the credit application should be rejected. One of the statistical methods that can be applied in examining the analysis of credit scoring is the Generalized Mean Distance-Based k-Nearest Neighbor (GMDKNN) classifier. Empirical study on this method uses 23,337 data of prospective debtor of unsecured credit in 2018, with the dependent variable being the credit scoring final decision and seven independent variables, i.e. age, child dependent, length of employment, age of the company, income, loan proposed, and duration of credit. Based on the feature selection test, all independent variables are significantly taking effect on the credit scoring final decision. The best classification model is obtained in the parameters k = 137 and p = -1 with the classification performance metrics represented by the values of APER = 0,2580, accuracy = 74,20%, sensitivity = 0,6083, specificity = 0,8393, AUC = 0,7238, and G-Mean = 0,7146.Keywords: Unsecured Credit, credit scoring, classification, Generalized Mean Distance-Based k-Nearest Neighbor (GMDKNN).
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Lawend, Haider O., Anuar Muad, and Aini Hussain. "An Improved Flexible Partial Histogram Bayes Learning Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 975. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp975-986.
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<em>This paper presents a proposed supervised classification technique namely flexible partial histogram Bayes (fPHBayes) learning algorithm. In our previous work, partial histogram Bayes (PHBayes) learning algorithm showed some advantages in the aspects of speed and accuracy in classification tasks. However, its accuracy declines when dealing with small number of instances or when the class feature distributes in wide area. In this work, the proposed fPHBayes solves these limitations in order to increase the classification accuracy. fPHBayes was analyzed and compared with PHBayes and other standard learning algorithms like first nearest neighbor, nearest subclass mean, nearest class mean, naive Bayes and Gaussian mixture model classifier. The experiments were performed using both real data and synthetic data considering different number of instances and different variances of Gaussians. The results showed that fPHBayes is more accurate and flexible to deal with different number of instances and different variances of Gaussians as compared to PHBayes.</em>
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Hussain, Altaf, Ijaz Ullah, and Tariq Hussain. "The Approach of Data Mining." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 10, no. 4 (2022): 339–59. http://dx.doi.org/10.14201/adcaij2021104339359.
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The concept of data mining is to classify and analyze the given data and to examine it clearly understandable and discoverable for the learners and researchers. The different types of classifiers are there exist to classify a data accordingly for the best and accurate results. Taking a primary data, and then classifying it into different portions of parts, then to analyze and remove any ambiguities from it and finally make it possible for understanding. With this process, that data will become secondary from primary and will called information. So, the classifiers are doing the same strategy for the solution and accuracy of the data. In this paper, different data mining approaches have been used by applying different classifiers on the taken data set. The data-set consists of 500 candidates’ segregated data for the analysis and evaluation to perfectly classify and to show the accurate results by using the proposed Algorithms. The data mining approaches have been used in which HUGO (Highly Undetectable steGO) Algorithm, Naïve Bayes Classification, k-nearest neighbors and Logistic Regression are used with the extension of the other classification methods that are Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) as classifiers. These classifiers are given names for further analysis that are Classifier-1 and Classifier-2 respectively. Along with these, a tool is used named WEKA (Waikato Environment for Knowledge Analysis) for the analysis of the classifier-1 and 2. For performance evaluation and analysis the parameters are used for best classification that which classifier has given best performance and why. These parameters are RRSE (Root Relative Square Error), RAE (Relative Absolute Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error). For the best and outstanding accuracy of the proposed work, these parameters have been tested under the simulation environment along with the incorrect, correct classifying and the %age has been witnessed and calculated. From simulation results based on RRSE, RAE, MAE and RMSE, it has been shown that classifier-1 has given outstanding performance among the others and has been placed in highest priority.
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Pavani, K. "Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 897–910. http://dx.doi.org/10.47059/revistageintec.v11i2.1723.
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Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was used to create a classifier by combining individual classifiers. The significance value achieved for finding the accuracy and precision was 0.445 and 0.754 respectively. Results and Discussions: Detection of vehicles in high speed videos is performed by using Haar Cascade which has mean accuracy with 85.22% and mean precision with 90.63% and 60% of mean accuracy and 58.53% mean precision in KNN classifiers. Conclusion: The performance of the Haar Cascade appears to be better than KNN in terms of both Accuracy and Precision.
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Lubaib. "PHONOCARDIOGRAM BASED DIAGNOSTIC SYSTEM." Bioscience & Engineering: An International Journal (BIOEJ) 2, July (2019): 10. https://doi.org/10.5281/zenodo.2650720.
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A Phonocardiogram or PCG is a plot of high fidelity recording of the sounds and murmurs made by the heart with the help of the machine called phonocardiograph. It has developed continuously to perform an important role in the proper and accurate diagnosis of the defects of the heart. As usually with the stethoscope, it requires highly and experienced physicians to read the phonocardiogram. A diagnostic system based on Artificial Neural Networks (ANN) is implemented as a detector and classifier of heart diseases. The output of the system is the classification of the sound as either normal or abnormal, if it is abnormal what type of abnormality is present. In this paper, Based on the extracted time domain and frequency domain features such as energy, mean, variance and Mel Frequency Cepstral Coefficients (MFCC) various heart sound samples are classified using Support Vector Machine (SVM), K Nearest Neighbour (KNN), Bayesian and Gaussian Mixture Model (GMM) Classifiers. The data used in this paper was obtained from Michigan university website.
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Azmi, Nur Liyana, Noor Azlyn Ab Ghafar, Khairul Affendy Md Nor, and Nor Hidayati Diyana Nordin. "Classification of Muscle Fatigue during Prolonged Driving." ELEKTRIKA- Journal of Electrical Engineering 21, no. 3 (2022): 40–46. http://dx.doi.org/10.11113/elektrika.v21n3.376.
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Driving has become essential in transporting people from one place to another. However, prolonged driving could cause muscle fatigue, leading to drowsiness and microsleep. Electromyography is an important type of electro-psychological signal that is used to measure electrical activity in muscles. The current study attempted to use machine learning algorithms to classify EMG signals recorded from the trapezius muscle of 10 healthy subjects in non-fatigue and fatigue conditions. The EMG signals and the time when muscle fatigue was experienced by the subjects were recorded. The mean frequency and median frequency of the EMG signals were extracted as dataset features. Six machine learning models were used for the classification: Logistic Regression, Support Vector Machine, Naïve Bayes, k-nearest Neighbour, Decision Tree and Random Forest. The results show that both the median and mean frequency are lower when fatigue conditions exist. In term of the classification performance, the Random Forest, Decision Tree and k-nearest Neighbour classifiers produced the accuracy levels of 85.00%, 83.75% and 81.25% respectively. Thus, the highest accuracy for classifying muscle fatigue due to prolonged driving was obtained by the Random Forest classifier, using both the median and mean frequency of the EMG signals. This method of using the mean and median frequency will be useful in classifying driver’s non-fatigue and fatigue conditions during prolonged driving.
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Pereira, Catarina, Federico Guede-Fernández, Ricardo Vigário, Pedro Coelho, José Fragata, and Ana Londral. "Image Analysis System for Early Detection of Cardiothoracic Surgery Wound Alterations Based on Artificial Intelligence Models." Applied Sciences 13, no. 4 (2023): 2120. http://dx.doi.org/10.3390/app13042120.
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Cardiothoracic surgery patients have the risk of developing surgical site infections which cause hospital readmissions, increase healthcare costs, and may lead to mortality. This work aims to tackle the problem of surgical site infections by predicting the existence of worrying alterations in wound images with a wound image analysis system based on artificial intelligence. The developed system comprises a deep learning segmentation model (MobileNet-Unet), which detects the wound region area and categorizes the wound type (chest, drain, and leg), and a machine learning classification model, which predicts the occurrence of wound alterations (random forest, support vector machine and k-nearest neighbors for chest, drain, and leg, respectively). The deep learning model segments the image and assigns the wound type. Then, the machine learning models classify the images from a group of color and textural features extracted from the output region of interest to feed one of the three wound-type classifiers that reach the final binary decision of wound alteration. The segmentation model achieved a mean Intersection over Union of 89.9% and a mean average precision of 90.1%. Separating the final classification into different classifiers was more effective than a single classifier for all the wound types. The leg wound classifier exhibited the best results with an 87.6% recall and 52.6% precision.
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B.Meena, Preeth, and Radha, P. "Disease Classification and Prediction using Ensemble Machine Learning Classification Algorithm." International Journal of Recent Technology and Engineering 9, no. 6 (2021): 202–14. http://dx.doi.org/10.35940/ijrte.f5507.039621.
Full textAbstract:
In today’s scenario, disease prediction plays an important role in medical field. Early detection of diseases is essential because of the fast food habits and life. In my previous study for predicting diseases using radiology test report , and to classify the disease as positive or negative three classifiers Naïve Bayes (NB), Support Vector Machine (SVM) and Modified Extreme Learning Machine (MELM was used to increase the accuracy of results. To increase the efficiency of predicting the disease and to find which disease pricks the society, ensemble machine learning algorithm is used. The huge data from the healthcare industry were preprocessed., categorized and analyzed to find out and predict which patient to be treated and given priority and which hits the society the most. Ensemble machine learning's popularity in the medical industry is due to a variety of factors the Classifiers used are K Nearest Neighbors, Nearest Mean Classifier, Mean Feature Voting Classifier, KDtree KNN, Random Forest. To reduce the manual processes in medical field automating these processes has become important. Electronic medical records and significant advances in health care have given an opportunity to make find out which patients need to be given more importance. Several methodologies and techniques were used to preprocess the data in order to meet the study' requirements. To improve the performance of machine learning algorithms, feature selections were made using Tabu search. When ensemble prediction is combined with the Random Forest algorithm as the combiner, the results are more reliable. The aim of this study is to create a system to classify Medical records whether it is diseased or not and find out which disease rate has increased. This research will help the society to an individual to get treated easily and take preventive measures to avoid diseases.