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Journal articles on the topic 'Multi-category classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Li, Luoqing, Chuanwu Yang, and Qiwei Xie. "1D embedding multi-category classification methods." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 02 (March 2016): 1640006. http://dx.doi.org/10.1142/s0219691316400063.

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In this paper, we propose a novel semi-supervised multi-category classification method based on one-dimensional (1D) multi-embedding. Based on the multiple 1D embedding based interpolation technique, we embed the high-dimensional data into several different 1D manifolds and perform binary classification firstly. Then we construct the multi-category classifiers by means of one-versus-rest and one-versus-one strategies separately. A weight strategy is employed in our algorithm for improving the classification performance. The proposed method shows promising results in the classification of handwritten digits and facial images.

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Zhong, Jincheng, and Shuhui Chen. "Efficient multi-category packet classification using TCAM." Computer Communications 169 (March 2021): 1–10. http://dx.doi.org/10.1016/j.comcom.2020.12.027.

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Paek, E. G., J. R. Wullert II, and J. S. Patel. "Optical learning machine for multi-category classification." Optics News 15, no. 12 (December 1, 1989): 28. http://dx.doi.org/10.1364/on.15.12.000028.

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R Udaya Prakash, Muthukrishnan. "Designing a better Support Vector Machines Classification Model for Multi-Class Category." International Journal of Science and Research (IJSR) 12, no. 2 (February 5, 2023): 905–9. http://dx.doi.org/10.21275/sr22517170244.

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Hill, S. I., and A. Doucet. "A Framework for Kernel-Based Multi-Category Classification." Journal of Artificial Intelligence Research 30 (December 12, 2007): 525–64. http://dx.doi.org/10.1613/jair.2251.

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A geometric framework for understanding multi-category classification is introduced, through which many existing 'all-together' algorithms can be understood. The structure enables parsimonious optimisation, through a direct extension of the binary methodology. The focus is on Support Vector Classification, with parallels drawn to related methods. The ability of the framework to compare algorithms is illustrated by a brief discussion of Fisher consistency. Its utility in improving understanding of multi-category analysis is demonstrated through a derivation of improved generalisation bounds. It is also described how this architecture provides insights regarding how to further improve on the speed of existing multi-category classification algorithms. An initial example of how this might be achieved is developed in the formulation of a straightforward multi-category Sequential Minimal Optimisation algorithm. Proof-of-concept experimental results have shown that this, combined with the mapping of pairwise results, is comparable with benchmark optimisation speeds.

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Tan, Jie, Xiaomin Chen, Guansheng Du, Qiaohui Luo, Xiao Li, Yaqing Liu, Xiao Liang, and Jianmin Wu. "Multi-dimensional on-particle detection technology for multi-category disease classification." Chemical Communications 52, no. 17 (2016): 3490–93. http://dx.doi.org/10.1039/c5cc09419d.

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Su, Hao, Zhiping Lin, and Lei Sun. "Extraction of category orthonormal subspace for multi-class classification." Journal of the Franklin Institute 358, no. 9 (June 2021): 5089–112. http://dx.doi.org/10.1016/j.jfranklin.2021.03.029.

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Liu, Defu, Jiayi Zhao, Jinzhao Wu, Guowu Yang, and Fengmao Lv. "Multi-category classification with label noise by robust binary loss." Neurocomputing 482 (April 2022): 14–26. http://dx.doi.org/10.1016/j.neucom.2022.01.031.

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Kang, Jianhong. "Earthquake Information Tracking Based on the Multi-category Classification Method." Journal of Information and Computational Science 12, no. 7 (May 1, 2015): 2647–53. http://dx.doi.org/10.12733/jics20105808.

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Shi, Wei, Yanghe Feng, Guangquan Cheng, Shixuan Liu, and Zhong Liu. "Multi-category Classification Problem Oriented Subsampling-Based Active Learning Method." Journal of Physics: Conference Series 1631 (September 2020): 012003. http://dx.doi.org/10.1088/1742-6596/1631/1/012003.

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Jayadeva, Reshma Khemchandani, and Suresh Chandra. "Fuzzy multi-category proximal support vector classification via generalized eigenvalues." Soft Computing 11, no. 7 (August 10, 2006): 679–85. http://dx.doi.org/10.1007/s00500-006-0130-2.

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Saigal, Pooja, Reshma Rastogi, and Suresh Chandra. "Semi-supervised Weighted Ternary Decision Structure for Multi-category Classification." Neural Processing Letters 52, no. 2 (August 7, 2020): 1555–82. http://dx.doi.org/10.1007/s11063-020-10323-7.

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Suresh, S., N. Sundararajan, and P. Saratchandran. "Risk-sensitive loss functions for sparse multi-category classification problems." Information Sciences 178, no. 12 (June 2008): 2621–38. http://dx.doi.org/10.1016/j.ins.2008.02.009.

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Zhang, Z., M. Y. Yang, and M. Zhou. "MULTI-SOURCE MULTI-SCALE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR REMOTE SENSING IMAGE CLASSIFICATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 293–300. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-293-2015.

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Fusion of remote sensing images and LiDAR data provides complimentary information for the remote sensing applications, such as object classification and recognition. In this paper, we propose a novel multi-source multi-scale hierarchical conditional random field (MSMSH-CRF) model to integrate features extracted from remote sensing images and LiDAR point cloud data for image classification. MSMSH-CRF model is then constructed to exploit the features, category compatibility of multi-scale images and the category consistency of multi-source data based on the regions. The output of the model represents the optimal results of the image classification. We have evaluated the precision and robustness of the proposed method on airborne data, which shows that the proposed method outperforms standard CRF method.

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Valujavičiūtė, Emilija. "Daugiaklasių duomenų klasifikavimo metodų tyrimas." Jaunųjų mokslininkų darbai 52, no. 2 (April 9, 2024): 50–59. http://dx.doi.org/10.15388/jmd.2022.2.5.

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The article analyzes the impact of the chosen method of model application on the classification of multi-label texts written in the Lithuanian language. The article presents a study of mult-label data classification methods in Lithuanian, which includes an analysis of the accuracy of the application of data classification methods for the automatic classification of multiclass texts written in Lithuanian. The classification methods, evaluation criteria, their applicability and the principles of data preparation for classification are reviewed. After preparing the text data for classification tasks, 44 combinations of classifiers were formed for the study and classification was performed using 3 different methods of multi-label data classification: category detection, category membership and category combination detection. The results obtained are compared in terms of time and classification accuracy, identifying the best performing classifiers and identifying the differences and advantages of the classification methods used.

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Jokić, Davor. "Field classification in Dimensions." Textile & leather review 2, no. 3 (September 9, 2019): 145–53. http://dx.doi.org/10.31881/tlr.2019.31.

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Given the latest research on Dimensions classification, this article discusses the novelty of such classification in the field of textile technology from the standpoint of Croatian scientific career advancement system. New machine learning article based classification system is compared to a traditional journal based classification system brought by the Web of Science and Scopus in terms of evaluation significance. The starting point of assigned category comparison were 13 journals indexed in the Web of Science in just one common category - Materials science, Textiles. Since Scopus does not have a unique category for the textile technology a list of 11 assigned categories was put in the comparison. Lastly, 58 research fields assigned to the articles published in mentioned journals indexed in Dimensions were analyzed for validity. Results show that the unique category of Textiles in Web of Science fully fits the field of textile technology from Croatian point of view. Scopus model with multi category assignment is not so reliable and useful in field evaluation. Lastly, Dimensions with its novel approach failed to validly classify indexed publications.

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Singh, Amritpal, and Sunil Kumar Chhillar. "News Category Classification Using Distinctive Bag of Words and ANN Classifier." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 311. http://dx.doi.org/10.23956/ijermt.v6i6.288.

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Category classification, for news, is a multi-label text classification problem. The goal is to assign one or more categories to a news article. A standard technique in multi-label text classification is to use a set of binary classifiers. For each category, a classifier is used to give a “yes” or “no” answer on if the category should be assigned to a text. Some of the standard algorithms for text classification that are used for binary classifiers include Naive Bayesian Classifiers, Support Vector Machines, artificial neural networks etc. In this distinctive bag of words have been used as feature set based on high frequency word tokens found in individual category of news. The algorithm presented in this work is based on a keyword extraction algorithm that is capable of dealing with English language in which different news categories i.e. Business, entertainment, politics, sports etc. has been considered. Intra-class news classification has been carried out in which Cricket and Football in sports category has been selected to verify the performance of the algorithm. Experimental results shows high classification rate in describing category of a news document.

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Li, Linguo, Shujing Li, and Jian Su. "A Multi-Category Brain Tumor Classification Method Bases on Improved ResNet50." Computers, Materials & Continua 69, no. 2 (2021): 2355–66. http://dx.doi.org/10.32604/cmc.2021.019409.

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Xie, Juanying, Kate Hone, Weixin Xie, Xinbo Gao, Yong Shi, and Xiaohui Liu. "Extending twin support vector machine classifier for multi-category classification problems." Intelligent Data Analysis 17, no. 4 (June 19, 2013): 649–64. http://dx.doi.org/10.3233/ida-130598.

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Zhang, Yanyu, and Jialiang Li. "COMBINING MULTIPLE MARKERS FOR MULTI-CATEGORY CLASSIFICATION: AN ROC SURFACE APPROACH." Australian & New Zealand Journal of Statistics 53, no. 1 (March 2011): 63–78. http://dx.doi.org/10.1111/j.1467-842x.2011.00603.x.

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Soltani, Abolfazl, Seyed Mohammad Ahadi, Neda Faraji, and Saeed Sharifian. "Designing efficient discriminant functions for multi-category classification using evolutionary methods." Neurocomputing 173 (January 2016): 1885–97. http://dx.doi.org/10.1016/j.neucom.2015.08.093.

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Jayadeva, Reshma Khemchandani, and Suresh Chandra. "Fuzzy linear proximal support vector machines for multi-category data classification." Neurocomputing 67 (August 2005): 426–35. http://dx.doi.org/10.1016/j.neucom.2004.09.002.

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23

Liu, Yang, Qince Li, Kuanquan Wang, Jun Liu, Runnan He, Yongfeng Yuan, and Henggui Zhang. "Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding." Biosensors 11, no. 11 (November 14, 2021): 453. http://dx.doi.org/10.3390/bios11110453.

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Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large number of possible label combinations and the imbalance among categories. Furthermore, the task of multi-label ECG classification is cost-sensitive, a fact that has usually been ignored in previous studies on the development of the model. To address these problems, in this work, we propose a novel deep learning model–based learning framework and a thresholding method, namely category imbalance and cost-sensitive thresholding (CICST), to incorporate prior knowledge about classification costs and the characteristic of category imbalance in designing a multi-label ECG classifier. The learning framework combines a residual convolutional network with a class-wise attention mechanism. We evaluate our method with a cost-sensitive metric on multiple realistic datasets. The results show that CICST achieved a cost-sensitive metric score of 0.641 ± 0.009 in a 5-fold cross-validation, outperforming other commonly used thresholding methods, including rank-based thresholding, proportion-based thresholding, and fixed thresholding. This demonstrates that, by taking into account the category imbalance and predefined cost information, our approach is effective in improving the performance and practicability of multi-label ECG classification models.

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Zhang, Xuelei, Xinyu Song, Ao Feng, and Zhengjie Gao. "Multi-Self-Attention for Aspect Category Detection and Biomedical Multilabel Text Classification with BERT." Mathematical Problems in Engineering 2021 (November 30, 2021): 1–6. http://dx.doi.org/10.1155/2021/6658520.

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Multilabel classification is one of the most challenging tasks in natural language processing, posing greater technical difficulties than single-label classification. At the same time, multilabel classification has more natural applications. For individual labels, the whole piece of text has different focuses or component distributions, which require full use of local information of the sentence. As a widely adopted mechanism in natural language processing, attention becomes a natural choice for the issue. This paper proposes a multilayer self-attention model to deal with aspect category and word attention at different granularities. Combined with the BERT pretraining model, it achieves competitive performance in aspect category detection and electronic medical records’ classification.

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Chen, Jindong, and Xijin Tang. "Exploring Societal Risk Classification of the Posts of Tianya Club." International Journal of Knowledge and Systems Science 5, no. 1 (January 2014): 36–48. http://dx.doi.org/10.4018/ijkss.2014010104.

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To identify the societal risk category of the posts of Tianya Club, several studies are carried out toward the posts of Tianya Club. With 2-month manually risk labeled new posts published during December of 2011 to January of 2012, statistical analysis of posts is conducted at first. Later, similarity analysis of posts from one risk category, different risk categories and published on different days are implemented. Finally, multi-class classification of posts using support vector machine (SVM) with different training set is tested. The statistical analysis and similarity analysis reveals the difficulties in multi-class classification of the posts of Tianya Club. The multi-class predictive results indicate that SVM could be applied to multi-class classification of posts, but still need further exploitation.

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Xu, Yanping. "A Deep Learning-Based Cluster Analysis Method for Large-Scale Multi-Label Images." Traitement du Signal 39, no. 3 (June 30, 2022): 931–37. http://dx.doi.org/10.18280/ts.390319.

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Large-scale multi-label image classification requires determining the presence or absence of a target object in a large number of sample images. For highly specialized and complex multi-label image sets, it is especially important to ensure the accuracy of image classification. Traditional deep learning models usually don’t take into account image-label correlation constraints when classifying multi-label images, and the strategy of classifying images based only on their own features greatly limits the model performance. In this context, this paper focuses a deep learning-based cluster analysis method for large-scale multi-label images. We constructed a model for large-scale multi-label image category recognition, which consists of a global image feature extraction module, a feature activation vector generation module and an image category inter-label connection module. Using a graph convolutional network (GCN), we aggregated the information of image category label nodes in the constructed multi-label graph structure, while exploring the correlation between image category labels. A detailed description is presented on how to introduce the attention mechanism into the constructed model mentioned above for image category recognition. Experimental results have validated the effectiveness of the constructed model.

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FARAOUN, K. M., and A. BOUKELIF. "GENETIC PROGRAMMING APPROACH FOR MULTI-CATEGORY PATTERN CLASSIFICATION APPLIED TO NETWORK INTRUSIONS DETECTION." International Journal of Computational Intelligence and Applications 06, no. 01 (March 2006): 77–99. http://dx.doi.org/10.1142/s1469026806001812.

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The present paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically co-evolve a population of nonlinear transformations on the input data to be classified, and map them to a new space with reduced dimension in order to get a maximum inter-classes discrimination. It is much easier to classify the new samples from the transformed data. Contrary to the existing GP-classification techniques, the proposed one uses a dynamic repartition of the transformed data in separated intervals, the efficiency of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were performed using the Fisher's Iris dataset. After that, the KDD'99 Cup dataset was used to study the intrusion detection and classification problem. The results demonstrate that the proposed genetic approach outperforms the existing GP-classification methods, and provides improved results compared to other existing techniques.

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Sharaff, Aakanksha, and Naresh Kumar Nagwani. "ML-EC2." International Journal of Web-Based Learning and Teaching Technologies 15, no. 2 (April 2020): 19–33. http://dx.doi.org/10.4018/ijwltt.2020040102.

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A multi-label variant of email classification named ML-EC2 (multi-label email classification using clustering) has been proposed in this work. ML-EC2 is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping technique. It is an example of classification using text clustering technique. It studies the problem where each email cluster represents a single class label while it is associated with set of cluster labels. It is multi-label text-clustering-based classification algorithm in which an email cluster can be mapped to more than one email category when cluster label matches with more than one category term. The algorithm will be helpful when there is a vague idea of topic. The performance parameters Entropy and Davies-Bouldin Index are used to evaluate the designed algorithm.

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Dreižienė, Lina, Kęstutis Dučinskas, and Laura Paulionienė. "Correct Classification Rates in Multi-Category Discriminant Analysis of Spatial Gaussian Data." Open Journal of Statistics 05, no. 01 (2015): 21–26. http://dx.doi.org/10.4236/ojs.2015.51003.

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Niu, Y. M., Y. S. Wong, G. S. Hong, and T. I. Liu. "Multi-Category Classification of Tool Conditions Using Wavelet Packets and ART2 Network." Journal of Manufacturing Science and Engineering 120, no. 4 (November 1, 1998): 807–16. http://dx.doi.org/10.1115/1.2830224.

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This paper proposes a new approach for multi-category identification of turning tool conditions. It uses the time-frequency feature information of the AE signal obtained from best-basis wavelet packet analysis. By applying the philosophy of divide-and-conquer and a local wavelet packet extraction technique, acoustic emission (AE) signals from turning process have been separated into transient and continuous components. The transient and continuous AE components are used respectively for transient tool conditions and tool wear identification. For transient tool condition identification, a 16-element feature vector derived from the frequency band value of wavelet packet coefficients in the time-frequency phase plane is used to identify tool fracture, chipping and chip breakage through an ART2 network. To identify tool wear status, spectral and statistical analysis techniques have been employed to extract three primary features: the frequency band power at 300 kHz–600 kHz, the skew and kurtosis. The mean and standard deviation within a moving window of the primary features are then computed to give three secondary features. The six features form the inputs to an ART2 neural network to identify fresh and worn state of the tool. Cutting experimental results have shown that this approach is highly successful in identifying both the transient and progressive tool wear states over a wide range of turning conditions.

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Dreižienė, Lina, and Kęstutis Dučinskas. "Error Rates in Multi-category Classification of the Spatial Multivariate Gaussian Data." Procedia Environmental Sciences 26 (2015): 78–81. http://dx.doi.org/10.1016/j.proenv.2015.05.003.

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Gandomkar, Ziba, Patrick C. Brennan, and Claudia Mello-Thoms. "MuDeRN: Multi-category classification of breast histopathological image using deep residual networks." Artificial Intelligence in Medicine 88 (June 2018): 14–24. http://dx.doi.org/10.1016/j.artmed.2018.04.005.

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Xu, Miao, Hongfei Liu, and Hongbo Yang. "Ensemble learning based approach for traffic incident detection and multi-category classification." Engineering Applications of Artificial Intelligence 132 (June 2024): 107933. http://dx.doi.org/10.1016/j.engappai.2024.107933.

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Wang, Yun, Kang Tian, Yanghan Gao, Bowen Hu, Xiaoyang Li, and Yadong Zhou. "Enhancing the Performance of Multi-Category Text Classification via Label Relation Mining." IEEE Access 12 (2024): 61433–42. http://dx.doi.org/10.1109/access.2024.3394853.

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Jin, Jun, Dasha Hu, Wei Pu, Yining Luo, and Xinyue Feng. "Few-shot learning with task adaptation for multi-category gastrointestinal endoscopy classification." Biomedical Signal Processing and Control 95 (September 2024): 106387. http://dx.doi.org/10.1016/j.bspc.2024.106387.

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Lin, Yo Hsien, and Hao En Chueh. "Applying Fuzzy Statistics and Clustering Analysis to Construct a Document Multi-Classification Model." Applied Mechanics and Materials 44-47 (December 2010): 3370–73. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3370.

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Document classification is the procedure that a document is assigned into one of the predefined categories according to its content. In many cases, the content of a document may be involved in more than one issue; therefore it is reasonable to assign a document to more than one category. This kind of categorizing procedure is called multi-classification. Most of the document classification models have been designed for dealing with single-classification cases, therefore, a document multi-classification model is proposed. In this paper, the concept of fuzzy statistics analysis is used with clustering analysis to carry out the document multi-classification task. The results of experiment show that the performance of our multi-classification model is better.

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Jiang, Yujian, Lin Song, Junming Zhang, Yang Song, and Ming Yan. "Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals." Sensors 22, no. 15 (August 5, 2022): 5855. http://dx.doi.org/10.3390/s22155855.

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Gesture recognition based on wearable devices is one of the vital components of human–computer interaction systems. Compared with skeleton-based recognition in computer vision, gesture recognition using wearable sensors has attracted wide attention for its robustness and convenience. Recently, many studies have proposed deep learning methods based on surface electromyography (sEMG) signals for gesture classification; however, most of the existing datasets are built for surface EMG signals, and there is a lack of datasets for multi-category gestures. Due to model limitations and inadequate classification data, the recognition accuracy of these methods cannot satisfy multi-gesture interaction scenarios. In this paper, a multi-category dataset containing 20 gestures is recorded with the help of a wearable device that can acquire surface electromyographic and inertial (IMU) signals. Various two-stream deep learning models are established and improved further. The basic convolutional neural network (CNN), recurrent neural network (RNN), and Transformer models are experimented on with our dataset as the classifier. The CNN and the RNN models’ test accuracy is over 95%; however, the Transformer model has a lower test accuracy of 71.68%. After further improvements, the CNN model is introduced into the residual network and augmented to the CNN-Res model, achieving 98.24% accuracy; moreover, it has the shortest training and testing time. Then, after combining the RNN model and the CNN-Res model, the long short term memory (LSTM)-Res model and gate recurrent unit (GRU)-Res model achieve the highest classification accuracy of 99.67% and 99.49%, respectively. Finally, the fusion of the Transformer model and the CNN model enables the Transformer-CNN model to be constructed. Such improvement dramatically boosts the performance of the Transformer module, increasing the recognition accuracy from 71.86% to 98.96%.

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S. Tidake, Vaishali, and Shirish S. Sane. "Multi-label Classification: a survey." International Journal of Engineering & Technology 7, no. 4.19 (November 27, 2018): 1045. http://dx.doi.org/10.14419/ijet.v7i4.19.28284.

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Wide use of internet generates huge data which needs proper organization leading to text categorization. Earlier it was found that a document describes one category. Soon it was realized that it can describe multiple categories simultaneously. This scenario reveals the use of multi-label classification, a supervised learning approach, which assigns a predefined set of labels to an object by looking at its characteristics. Earlier used in text categorization, but soon it became the choice of researchers for wide applications like marketing, multimedia annotation, bioinformatics. Two most common approaches for multi-label classification are transformation which takes the benefit of existing single label classifiers preceded by converting multi-label data to single label, or an adaptation which designs classifiers which handle multi-label data directly. Another popular approach is ensemble of multiple classifiers taking votes of all. Other approaches are also available namely algorithm independent and algorithm dependent approach. Based on results produced, suitable metric is used for example or label wise evaluation which depends on whether prediction is binary or ranking. Every approach offers benefits and issues like loss of label dependency in transformation, complexity in case of adaptation, improvement in results using ensemble which should be considered during design of underlying application.

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Liu, Chengliang, Jie Wen, Xiaoling Luo, and Yong Xu. "Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (June 26, 2023): 8816–24. http://dx.doi.org/10.1609/aaai.v37i7.26060.

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As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable for various pattern recognition tasks. In this complex representation learning problem, three main challenges can be characterized as follows: i) How to learn consistent representations of samples across all views? ii) How to exploit and utilize category correlations of multi-label to guide inference? iii) How to avoid the negative impact resulting from the incompleteness of views or labels? To cope with these problems, we propose a general multi-view multi-label learning framework named label-guided masked view- and category-aware transformers in this paper. First, we design two transformer-style based modules for cross-view features aggregation and multi-label classification, respectively. The former aggregates information from different views in the process of extracting view-specific features, and the latter learns subcategory embedding to improve classification performance. Second, considering the imbalance of expressive power among views, an adaptively weighted view fusion module is proposed to obtain view-consistent embedding features. Third, we impose a label manifold constraint in sample-level representation learning to maximize the utilization of supervised information. Last but not least, all the modules are designed under the premise of incomplete views and labels, which makes our method adaptable to arbitrary multi-view and multi-label data. Extensive experiments on five datasets confirm that our method has clear advantages over other state-of-the-art methods.

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Bharathi, S., and P. Venkatesan. "Enhanced Classification of Faults of Photovoltaic Module Through Generative Adversarial Network." International Journal of Electrical and Electronics Research 10, no. 3 (September 30, 2022): 579–84. http://dx.doi.org/10.37391/ijeer.100328.

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The faults occurring in the photo voltaic system has to be detected to make it work efficiently .To detect and classify the faults occurring in the photo voltaic module infrared images, electro luminescent images, photo luminescent images of photo voltaic module is used .Using infrared images around 11 faults of photovoltaic module such as cell ,cell-multi, hot-spot-multi , hot-spot, cracking, diode, diode-multi, vegetation, shadowing, off-line module and soiling faults can be detected. In addition to the original infra-red images (IR) available in the IR dataset, the IR images are generated for each and every category of faults by using generative adversarial networks (GAN’s) to increase the dataset size. 45000 images are generated by GAN’s. Later the images are used to train and test the convolution neural network. The dataset visualization of original and that of GAN generated images are done in 2-dimensional space using uniform manifold approximation and projection. In this work 12 categories of IR dataset are considered for classification in which 11 belongs to fault category and the remaining one is the normal category of images. In earlier work only 11 category of faults or less than that is considered for classification. Compared the results with the existing work and it is found that by enhancing the dataset size by GAN’s accuracy of 91.7 % is obtained during the classification of 8 categories of faults.

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Agarwal, Reshu, and Mandeep Mittal. "Inventory Classification Using Multi-Level Association Rule Mining." International Journal of Decision Support System Technology 11, no. 2 (April 2019): 1–12. http://dx.doi.org/10.4018/ijdsst.2019040101.

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Popular data mining methods support knowledge discovery from patterns that hold in relations. For many applications, it is difficult to find strong associations among data items at low or primitive levels of abstraction. Mining association rules at multiple levels may lead to more informative and refined knowledge from data. Multi-level association rule mining is a variation of association rule mining for finding relationships between items at each level by applying different thresholds at different levels. In this study, an inventory classification policy is provided. At each level, the loss profit of frequent items is determined. The obtained loss profit is used to rank frequent items at each level with respect to their category, content and brand. This helps inventory manager to determine the most profitable item with respect to their category, content and brand. An example is illustrated to validate the results. Further, to comprehend the impact of above approach in the real scenario, experiments are conducted on the exiting dataset.

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Park, Moon Ho. "Informant questionnaire on cognitive decline in the elderly (IQCODE) for classifying cognitive dysfunction as cognitively normal, mild cognitive impairment, and dementia." International Psychogeriatrics 29, no. 9 (May 31, 2017): 1461–67. http://dx.doi.org/10.1017/s1041610217000965.

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ABSTRACTBackground:The Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) is a reliable, validated informant-based instrument in screening for cognitive dysfunction. However, previous studies have evaluated only the ability to discriminate dichotomously, such as dementia from cognitively normal (CN) individuals or mild cognitive impairment (MCI) from CN. This study investigated the ability of the IQCODE to classify not only dichotomous but also multiple stages of cognitive dysfunction.Methods:We examined 228 consecutive participants (76 CN, 76 with MCI, and 76 with dementia). Receiver operating characteristic (ROC) curves determined dichotomous classification parameters. Multi-category ROC surfaces were evaluated to classify three stages of cognitive dysfunction.Results:Dichotomous classification using the ROC curve analyses showed that the area under the ROC curve was 0.91 for dementia from participants without dementia and 0.71 for MCI from CN. Simultaneous multi-category classification analyses showed that the volume under the ROC surface was 0.61 and the derived optimal cut-off points were 3.15 and 3.73 for CN, MCI, and dementia. The Youden index for the IQCODE was estimated as 0.51 and the derived optimal cut-off points were 3.33 and 3.70. The overall classification accuracy by the VUS was 58.3% and that by the Youden index 61.8%.Conclusions:IQCODE is useful to classify the dichotomous and multi-category stages of cognitive dysfunction.

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Li, Hengchao, Weiye Wang, Shaohui Ye, Yangjun Deng, Fan Zhang, and Qian Du. "A mixture generative adversarial network with category multi-classifier for hyperspectral image classification." Remote Sensing Letters 11, no. 11 (September 22, 2020): 983–92. http://dx.doi.org/10.1080/2150704x.2020.1804641.

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Guan, Qingji, and Yaping Huang. "Multi-label chest X-ray image classification via category-wise residual attention learning." Pattern Recognition Letters 130 (February 2020): 259–66. http://dx.doi.org/10.1016/j.patrec.2018.10.027.

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Khemchandani, Reshma, and Pooja Saigal. "Color image classification and retrieval through ternary decision structure based multi-category TWSVM." Neurocomputing 165 (October 2015): 444–55. http://dx.doi.org/10.1016/j.neucom.2015.03.074.

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Yan, Yi, WeiKai Zheng, Jian Bao, and Ran Liu. "An enhanced M-ary SVM algorithm for multi-category classification and its application." Neurocomputing 187 (April 2016): 119–25. http://dx.doi.org/10.1016/j.neucom.2015.08.101.

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Chen, Hong, and Luo-qing Li. "On the rate of convergence for multi-category classification based on convex losses." Science in China Series A: Mathematics 50, no. 11 (July 31, 2007): 1529–36. http://dx.doi.org/10.1007/s11425-007-0100-x.

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Suresh, S., S. Saraswathi, and N. Sundararajan. "Performance enhancement of extreme learning machine for multi-category sparse data classification problems." Engineering Applications of Artificial Intelligence 23, no. 7 (October 2010): 1149–57. http://dx.doi.org/10.1016/j.engappai.2010.06.009.

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Miften, Firas Sabar, Mohammed Diykh, Shahab Abdulla, Siuly Siuly, Jonathan H. Green, and Ravinesh C. Deo. "A new framework for classification of multi-category hand grasps using EMG signals." Artificial Intelligence in Medicine 112 (February 2021): 102005. http://dx.doi.org/10.1016/j.artmed.2020.102005.

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Tripathi, Nandita, Michael Oakes, and Stefan Wermter. "A Scalable Meta-Classifier Combining Search and Classification Techniques for Multi-Level Text Categorization." International Journal of Computational Intelligence and Applications 14, no. 04 (December 2015): 1550020. http://dx.doi.org/10.1142/s1469026815500200.

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Nowadays, documents are increasingly associated with multi-level category hierarchies rather than a flat category scheme. As the volume and diversity of documents grow, so do the size and complexity of the corresponding category hierarchies. To be able to access such hierarchically classified documents in real-time, we need fast automatic methods to navigate these hierarchies. Today’s data domains are also very different from each other, such as medicine and politics. These distinct domains can be handled by different classifiers. A document representation system which incorporates the inherent category structure of the data should also add useful semantic content to the data vectors and thus lead to better separability of classes. In this paper, we present a scalable meta-classifier to tackle today’s problem of multi-level data classification in the presence of large datasets. To speed up the classification process, we use a search-based method to detect the level-1 category of a test document. For this purpose, we use a category–hierarchy-based vector representation. We evaluate the meta-classifier by scaling to both longer documents as well as to a larger category set and show it to be robust in both cases. We test the architecture of our meta-classifier using six different base classifiers (Random forest, C4.5, multilayer perceptron, naïve Bayes, BayesNet (BN) and PART). We observe that even though there is a very small variation in the performance of different architectures, all of them perform much better than the corresponding single baseline classifiers. We conclude that there is substantial potential in this meta-classifier architecture, rather than the classifiers themselves, which successfully improves classification performance.

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