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Relevant bibliographies by topics / J48 Classifier

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Academic literature on the topic 'J48 Classifier'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "J48 Classifier"

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Saradha, S., and P. Sujatha. "Prediction of gestational diabetes diagnosis using SVM and J48 classifier model." International Journal of Engineering & Technology 7, no. 2.21 (2018): 323. http://dx.doi.org/10.14419/ijet.v7i2.21.12395.

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Knowledge Discovery in Databases (KDD) process is also known as data mining. It is a most powerful tool for medical diagnosis. Due to hormonal changes, diabetes may occur during pregnancy is referred as Gestational diabetes mellitus (GDM). Pregnant Women with GDM are at highest risk of future diabetes, especially type-2 diabetes. This paper focuses on designing an automated system for diagnosing gestational diabetes using hybrid classifiers as well as predicting the highest risk factors of getting Type 2 diabetes after delivery. One of the common predictive data mining tasks is classification.

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Taha Chicho, Bahzad, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, and Dilovan Assad Zebari. "Machine Learning Classifiers Based Classification For IRIS Recognition." Qubahan Academic Journal 1, no. 2 (2021): 106–18. http://dx.doi.org/10.48161/qaj.v1n2a48.

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Classification is the most widely applied machine learning problem today, with implementations in face recognition, flower classification, clustering, and other fields. The goal of this paper is to organize and identify a set of data objects. The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. The results of the comparison analysis showed that the K-nearest neighbors outperformed the other classifiers. Also, the random forest classifier worked better than the decision tree (j48). Finally, the best

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Sun, Pei Pei, Quan Yin Zhu, Lei Zhou, and Yong Jun Zhang. "Comparative Analysis of Text Categorizer on Science and Technology Intelligence." Applied Mechanics and Materials 530-531 (February 2014): 502–5. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.502.

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In order to more effectively classify the science and technology intelligence text, the idea that classifying science and technology intelligence text categorization based on different classifiers is proposed. The experiment is done with two thousand Chinese texts based on three different classifiers in this paper. Among these classifiers, the rate of correctly classified instances with NaiveBayes Classifier is 96.95 percent and J48 Classifiers is 97.59. The highest of three classifiers is SMO Classifier and its correct rate is 98.65 percent. According to the analysis of experimental results,

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Bohani, Farah Aqilah, Farah Syazwani Mohamed Rashid, Yuzi Mahmud, and Sitti Rachmawati Yahya. "ANALYZING THE IMPACT OF FEATURE SELECTION USING INFORMATION GAIN FOR AIRLINES' CUSTOMER SATISFACTION." MALAYSIAN JOURNAL OF COMPUTING (MJOC) 9, no. 1 (2024): 1673–89. http://dx.doi.org/10.24191/mjoc.v9i1.24163.

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Feature selection has become a focus of research in many fields that deal with machine learning and data mining because it makes classifiers cost-effective, faster, and more accurate. In this paper, the impact of feature selection using filter methods such as Information Gain is shown. The impact of feature selection has been analyzed based on the accuracy of two classifiers: J48 and Naïve Bayes. The Airline Customer Satisfaction datasets have been used for comparing with and without applying Information Gain. As a result, J48 achieved 0.33% and 0.29% improvements in accuracy after applying In

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Solanki, Yogendra Singh, Prasun Chakrabarti, Michal Jasinski, et al. "A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches." Electronics 10, no. 6 (2021): 699. http://dx.doi.org/10.3390/electronics10060699.

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Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine,

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Camargo, Flávio F., Edson E. Sano, Cláudia M. Almeida, José C. Mura, and Tati Almeida. "A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images." Remote Sensing 11, no. 13 (2019): 1600. http://dx.doi.org/10.3390/rs11131600.

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This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/P

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Asim Shahid, Muhammad, Muhammad Mansoor Alam, and Mazliham Mohd Su’ud. "A fact based analysis of decision trees for improving reliability in cloud computing." PLOS ONE 19, no. 12 (2024): e0311089. https://doi.org/10.1371/journal.pone.0311089.

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The popularity of cloud computing (CC) has increased significantly in recent years due to its cost-effectiveness and simplified resource allocation. Owing to the exponential rise of cloud computing in the past decade, many corporations and businesses have moved to the cloud to ensure accessibility, scalability, and transparency. The proposed research involves comparing the accuracy and fault prediction of five machine learning algorithms: AdaBoostM1, Bagging, Decision Tree (J48), Deep Learning (Dl4jMLP), and Naive Bayes Tree (NB Tree). The results from secondary data analysis indicate that the

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Waruwu, Jurisman, Wira Hadinata, Siska Febriyani, and Rini Wijayanti. "Data Mining Technique in Detecting and Predicting Cyber In Marketplace Sector." Jurnal Informatika 1, no. 1 (2022): 8–11. http://dx.doi.org/10.57094/ji.v1i1.350.

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Marketplace is one business solution that can be profitable, because it is not bound by time and place. However, marketplace can be misused by irresponsible parties, and can harm others. Then a pattern is needed to predict cybercrime in order to prevent it. To get a pattern, we can use data mining. This paper presents a general idea about the model of Data Mining techniques and diverse cybercrimes in market place applications. This paper implements data mining techniques like K-Means, Influenced Association Classifier and J48 Prediction tree for investigating the cybercrime data sets. K-means

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Aljammal, Ashraf H., Salah Taamneh, Ahmad Qawasmeh, and Hani Bani Salameh. "Machine Learning Based Phishing Attacks Detection Using Multiple Datasets." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 05 (2023): 71–83. http://dx.doi.org/10.3991/ijim.v17i05.37575.

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Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated,

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Mihǎescu, Marian Cristian, Paul Ştefan Popescu, and Dumitru Dan Burdescu. "J48 list ranker based on advanced classifier decision tree induction." International Journal of Computational Intelligence Studies 4, no. 3/4 (2015): 313. http://dx.doi.org/10.1504/ijcistudies.2015.072879.

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Book chapters on the topic "J48 Classifier"

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Thilagaraj, M., and M. Pallikonda Rajasekaran. "Epileptic Seizure Mining via Novel Empirical Wavelet Feature with J48 and KNN Classifier." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7566-7_23.

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Anzola Anzola, John P., Luz Andrea Rodriguez Rojas, and Giovanny M. Tarazona Bermudez. "State of the Art Construction Based on the J48 Classifier: Case Study of Internet of Things." In Lecture Notes in Business Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21009-4_36.

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Tien Bui, Dieu, Tien Chung Ho, Inge Revhaug, Biswajeet Pradhan, and Duy Ba Nguyen. "Landslide Susceptibility Mapping Along the National Road 32 of Vietnam Using GIS-Based J48 Decision Tree Classifier and Its Ensembles." In Cartography from Pole to Pole. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32618-9_22.

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Arslantas, Mustafa Kemal, Tunc Asuroglu, Reyhan Arslantas, et al. "Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICU." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_1.

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AbstractSerum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients, as suggested by The Surviving Sepsis Campaign, early lactate clearance-directed therapy is associated with decreased mortality; thus, serum lactate levels should be assessed. Monitoring a patient’s vital parameters and repetitive blood analysis may have deleterious effects on the patient and also bring an economic burden. Machine learning and trend analysis are gaining importance to overcome these issues. In this context, we aimed to investigate if a machine learning approach can predict lactate trends from non-invasive parameters of patients with sepsis. This retrospective study analyzed adult sepsis patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset. Inclusion criteria were two or more lactate tests within 6 h of diagnosis, an ICU stay of at least 24 h, and a change of ≥1 mmol/liter in lactate level. Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers were evaluated for lactate trend prediction. LMT algorithm outperformed other classifiers (AUC = 0.803; AUPRC = 0.921). J48 decision tree performed worse than the other methods when predicting constant trend. LMT algorithm with four features (heart rate, oxygen saturation, initial lactate, and time interval variables) achieved 0.80 in terms of AUC (AUPRC = 0.921). We can say that machine learning models that employ logistic regression architectures, i.e., LMT algorithm achieved good results in lactate trend prediction tasks, and it can be effectively used to assess the state of the patient, whether it is stable or improving.

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Andrade, Matheus Santos, and Jonathas Carvalho de Freitas. "Analysis of Performance Metrics on the Conjuncture of Intrusions in IEEE 802.11 Networks with Machine Learning at Hospital N.S.C." In CONNECTING EXPERTISE MULTIDISCIPLINARY DEVELOPMENT FOR THE FUTURE. Seven Editora, 2023. http://dx.doi.org/10.56238/connexpemultidisdevolpfut-116.

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The security present in IEEE 802.11 networks becomes more relevant every day. However, security on the IEEE 802.11 network has not kept pace with threats with as much significance. For this reason, the proposal arises to design an Intrusion Detection System-IDS based on machine learning that will be able to have self-improvement, since it will create a safe environment, capable of detecting all disguised threats, Deauthentication, Eapol -logoff (Eapol) and Beacon Flood, where they were launched on a real corporate network. With this, correlated the performance metrics, and among them, which values the quality of the classification, the Matthews Correlation Coefficient. The Deauthentication anomaly above the Naive Bayes classifier was obtained (88.71%), whereas the quality value of the Logistic Regression (Logistic) classifier was equated to (88.69%), and nevertheless, the J48 presented a lower value of (88.47%). Despite this, the identification of the Beacon Flood attack was due to the Naive Bayes algorithm showing the highest detection rate (100.00%), followed by Logistic (99.95%) and J48 having the lowest value (98.85 %). As a result, in the detection of the Eapol anomaly, the classifications presented similarity of (100.00%) and the others, with the presentation of a detection, due to non-anomalous data (Normal), the Naive Bayes was affected by (89.92 % ), followed by Logistic maintaining (89.89%), while J48 was tested with a lower rate (89.67%). With the study evidences provide the possibility that it is possible to develop an intrusion detection system based on wireless networks.

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Parvathala, Balakesava Reddy, A. Manikandan, P. Vijayalakshmi, M. Muzammil Parvez, S. Harihara Gopalan, and S. Ramalingam. "Bio-Inspired Metaheuristic Algorithm for Network Intrusion Detection System of Architecture." In Bio-Inspired Intelligence for Smart Decision-Making. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5276-2.ch004.

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By identifying different kinds of attacks and application misuse that firewalls normally aren't able to identify, network intrusion detection systems (IDS) are intended to keep computer networks safe. When creating a network intrusion detection system, feature selection techniques are crucial. Several bionic meta-heuristic algorithms are used to quickly categorize network traffic as problematic or normal, then decrease features to demonstrate higher accuracy. Thus, in order to detect frequent attacks, this research proposes a hybrid model of network intrusion detection system (IDS) based on an algorithm inspired by a hybrid bionic element. There are two goals for the suggested model. The first step is to minimize the number of features that are chosen in Network IDS. By combining biosensing metaheuristics with hybrid models, this objective is accomplished. The algorithms used in this chapter are particle swarm optimization (PSO), multiverse optimizer (MVO), grey wolf optimization (GWO), moth flame optimization (MFO), firefly algorithm (FFA), whale optimization algorithm (WOA), bat algorithm (BAT), genetic bee colony (GBC) algorithm, artificial bee colony algorithm (ABC), fish swarm algorithm (FSA), cat swarm optimization (CSO), artificial algae algorithm (AAA), elephant herd optimization (EHO), cuckoo search optimization algorithm (CSOA), lion optimization algorithm (LOA), and cuttlefish algorithm (CFA) algorithm. Using machine learning classifiers, the second objective is to identify frequent attacks. SVM (support vector machine), C4.5 (J48) decision trees, and RF (random forest) classifiers are used to accomplish this purpose. Thus, the goal of the suggested model is to pinpoint frequent attacks. The data indicates that J48 is the top classifier when it comes to model building time when compared to SVM and RF. The data indicates that when it came to feature reduction for classification, the MVO-BAT model decreased the features to 24, whereas the MFO-WOA and FFA-GWO models lowered the accuracy, sensitivity, and F-measure of all features to 15. The accuracy, sensitivity, and F-measure of each feature are the same for every classifier.

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Vasavi, S., T. Naga Jyothi, and V. Srinivasa Rao. "Moving Object Classification in a Video Sequence." In Applied Video Processing in Surveillance and Monitoring Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1022-2.ch004.

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Now-a-day's monitoring objects in a video is a major issue in areas such as airports, banks, military installations. Object identification and recognition are the two important tasks in such areas. These require scanning the entire video which is a time consuming process and hence requires a Robust method to detect and classify the objects. Outdoor environments are more challenging because of occlusion and large distance between camera and moving objects. Existing classification methods have proven to have set of limitations under different conditions. In the proposed system, video is divided into frames and Color features using RGB, HSV histograms, Structure features using HoG, DHoG, Harris, Prewitt, LoG operators and Texture features using LBP, Fourier and Wavelet transforms are extracted. Additionally BoV is used for improving the classification performance. Test results proved that SVM classifier works better compared to Bagging, Boosting, J48 classifiers and works well in outdoor environments.

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Sahu, Sanat Kumar, and A. K. Shrivas. "Comparative Study of Classification Models with Genetic Search Based Feature Selection Technique." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch040.

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Feature selection plays a very important role to retrieve the relevant features from datasets and computationally improves the performance of a model. The objective of this study is to evaluate the most important features of a chronic kidney disease (CKD) dataset and diagnose the CKD problem. In this research work, the authors have used a genetic search with the Wrapper Subset Evaluator method for feature selection to increase the overall performance of the classification model. They have also used Bayes Network, Classification and Regression Tree (CART), Radial Basis Function Network (RBFN) and J48 classifier for classification of CKD and non-CKD data. The proposed genetic search based feature selection technique (GSBFST) selects the best features from CKD dataset and compares the performance of classifiers with proposed and existing genetic search feature selection techniques (FSTs). All classification models give the better result with proposed GSBFST as compared to without FST and existing genetic search FSTs.

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Wong, Mu Lin, and Senthil S. "Development of Accurate and Timely Students' Performance Prediction Model Utilizing Heart Rate Data." In Advances in Computer and Electrical Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2772-6.ch007.

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Academic Performance Prediction models mustn't be accurate only, but timely too, to identify at-risk students at the earliest to provide remedy. Heart rate data of 50 students in 3 main courses are collected, processed, and analyzed to distinguish the difference between excellent students and at-risk students. Three of the 12 heart rate attributes were chosen to calculate the threshold values, which are used to predict at-risk students. Half of the at-risk students were identified after week 5. Later, the datasets were rebalanced. Using four Data Mining classifiers, six attributes were identified to be the best attributes for prediction model development. The datasets were then dimensionally reduced. Applying classification, half of the at-risk students were identified earliest around week 5 of the 12-week semester. J48 is the most robust classifier, compared to JRip, Multi-Level-Perceptron, and RandomForest, making accurate prediction on at-risk students earlier most of the time.

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R.S., Dhivya, and Sujatha P. "A New Perspective to Evaluate Machine Learning Algorithms for Predicting Employee Performance." In Intelligent Technologies for Automated Electronic Systems. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815179514124010013.

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Performance prediction is the forecast of future performance conditions based on past and present information. Forecasts can be made about companies, departments, systems, processes, and employees. This study focuses on assessing employee performance in terms of employee behavior, work, and growth potential. Organizations benefit when their employees perform well. Therefore, predicting employee performance plays an important role in a growing organization. To this end, we propose three machine learning algorithms: a support vector machine, a decision tree (j48), and a naive Bayes classifier. These can predict employee behavior in the workplace. Comparing the results, the Naive Bayes algorithm shows better results than the other two algorithms on the basis of metrics such as timeliness, error loss, and accuracy.

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Conference papers on the topic "J48 Classifier"

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Patil, Rupali, and V. M. Barkade. "Class-Specific Features Using J48 Classifier for Text Classification." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697473.

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Carvalho, Lucas, Maycon Silva, Edimilson Santos, and Daniel Guidoni. "On the Analysis of Machine Learning Classifiers to Detect Traffic Congestion in Vehicular Networks." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/eniac.2019.9290.

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Problems related to traffic congestion and management have become common in many cities. Thus, vehicle re-routing methods have been proposed to minimize the congestion. Some of these methods have applied machine learning techniques, more specifically classifiers, to verify road conditions and detect congestion. However, better results may be obtained by applying a classifier more suitable to domain. In this sense, this paper presents an evaluation of different classifiers applied to the identification of the level of road congestion. Our main goal is to analyze the characteristics of each clas

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Satyanarayana, Nimmala, Y. Ramadevi, and K. Koteswara Chari. "High blood pressure prediction based on AAA using J48 classifier." In 2018 Conference on Signal Processing And Communication Engineering Systems (SPACES). IEEE, 2018. http://dx.doi.org/10.1109/spaces.2018.8316330.

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Wu, Mingtao, Vir V. Phoha, Young B. Moon, and Amith K. Belman. "Detecting Malicious Defects in 3D Printing Process Using Machine Learning and Image Classification." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-67641.

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3D printing, or additive manufacturing, is a key technology for future manufacturing systems. However, 3D printing systems have unique vulnerabilities presented by the ability to affect the infill without affecting the exterior. In order to detect malicious infill defects in 3D printing process, this paper proposes the following: 1) investigate malicious defects in the 3D printing process, 2) extract features based on simulated 3D printing process images, and 3) an experiment of image classification with one group of non-defect infill image and the other group of defect infill training image f

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Yasin, Waheed, Hamidah Ibrahim, Nur Izura Udzir, and Nor Asilah Wati Abdul Hamid. "Intelligent Cooperative Least Recently Used Web Caching Policy based on J48 Classifier." In iiWAS '14: The 16th International Conference on Information Integration and Web-based Applications & Services. ACM, 2014. http://dx.doi.org/10.1145/2684200.2684299.

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Emon, Shimoul Uddin, Tahira Islam Trishna, Romana Rahman Ema, Gazi Imran Hossen Sajal, Shuvodip Kundu, and Tajul Islam. "Detection of Hepatitis Viruses Based on J48, KStar and Naïve Bayes Classifier." In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2019. http://dx.doi.org/10.1109/icccnt45670.2019.8944619.

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Nie, Chun yan, Ju Wang, Fang He, and Reika Sato. "Application of J48 Decision Tree Classifier in Emotion Recognition Based on Chaos Characteristics." In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Atlantis Press, 2015. http://dx.doi.org/10.2991/amcce-15.2015.330.

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Jevtić, Dragan, Kristijan Kuk, and Vladica Stojanović. "Application of machine learning in the analysis of the web proxy server in railway traffic environment." In Proceeding of scientific-expert Conference on Railway Railcon '24. University of Niš - Faculty of Mechanical Engineering, Niš, 2024. http://dx.doi.org/10.5937/railcon24087j.

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The introduction of machine learning and anomaly detection classifiers in the process of supervision in the IT industry, opens up a new dimension in traffic analytics and decision-making for interventions in short periods. Case, described in his paper, needed a proxy server control for certain clients in the intranet network, clients in the traffic and transportation domain. The importance of those positions lies in the fact that the operation and control of traffic processes on the railway are monitored through these workstations. Since the National CERT of the Republic of Serbia issued a not

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Senthilnayaki, B., K. Venkatalakshmi, and A. Kannan. "An intelligent intrusion detection system using genetic based feature selection and Modified J48 decision tree classifier." In 2013 Fifth International Conference on Advanced Computing (ICoAC). IEEE, 2013. http://dx.doi.org/10.1109/icoac.2013.6921918.

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Souza, Junior, Vanessa Weber, Ariadne Gonçalves, et al. "Viable Yeast Identification using Bag of Visual Words in Colored images." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13493.

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In this research it is reported a system to automate the process of identification of viable yeasts whose population control is a crucial task in the ethanol production process. The identification and counting of yeasts made by human vision under a light microscope, is repetitive and susceptible to errors. We used computer vision techniques such as BoVW, Color Coherence Vectors (CCV), Color Moments (CM), Bag-of-Color (BoC) and Opponent Color (OpC) were applied for extracting characteristics that were classified by the Naive Bayes, KNN, SVM and J48 algorithms in 2614 images of yeasts separated

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