Topic
C4.5 algorithm
About: C4.5 algorithm is a research topic. Over the lifetime, 1314 publications have been published within this topic receiving 39194 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article , the authors focused on the application of quantitative methods, specifically the J48 algorithm and Naïve Bayes algorithm, and found that these two algorithms have a fairly high accuracy value of 94% for naïve Bayes and 98% for J48.
Abstract: In the field of goods production, demand prediction is important. By doing sales predictions, companies can make calculations and forecasts for what raw materials are mostly ordered. J48 and Naïve Bayes algorithm are two popular machine learning technique. By using these two algorithms, this study aims to develop an accurate and more reliable predictive model that help the company to make data driven decision. This study focuses on the application of quantitative methods, specifically the J48 algorithm and Naïve Bayes algorithm. This research conducted 4 times testing on each algorithm. This study produces high accuracy values with the Naïve Bayes and J48 algorithms. Both algorithm results have a fairly high accuracy value of 94% for Naïve Bayes and 98% for J48. The findings of this study implicate that by using J48 and Naïve Bayes algorithm, company can make informed decisions lead to improved operational efficiency, cost-effective, and resource utilization.
TL;DR: A voting-based ensemble classifier has been proposed along with two base learners (namely, Random Forest and Rotation Forest) to progress the performance and experimental outcomes are presented that validate the effectiveness of the method to well-known competitive approaches.
Abstract: Machine learning (ML) is a prominent and extensively researched field in the artificial intelligence area which assists to strengthen the accomplishment of classification. In this study, the main idea is to provide the classification and analysis of ML and Ensemble Learning (EL) algorithms. To support this idea, six supervised ML algorithms, C4.5 (J48), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB) and One Rule (OneR) in addition the five UCI Datasets of ML Repository, are being applied that demonstrates the robustness and effectiveness of numerous approaches. In this paper, a voting-based ensemble classifier has been proposed along with two base learners (namely, Random Forest and Rotation Forest) to progress the performance. Whereas, for analytical procedures, significant parameters have been considered: Accuracy, Area under Curve (AUC), recall, precision, and F-measure values. Hence, the prime objective of this research is to obtain binary classification and efficiency by conducting the progress of ML and EL approaches. We present experimental outcomes that validate the effectiveness of our method to well-known competitive approaches. Image recognition and ML challenges, such as binary classification, can be solved using this method.
10 Nov 2022
TL;DR: In this article , a novel increment processing addition to the Map Reduce, the most extensively used methodology for mining the big data by using the Naive Bayes, the J48, and the Random Forest algorithms.
Abstract: Data mining applications have become outdated and outmoded in recent years. The use of incremental processing to refresh mining results is a promising method. It makes use of previously saved states to save time and energy on re-computation. In this research, we offer a novel increment processing addition to the Map Reduce, the most extensively used methodology for mining the big data by using the Naive Bayes, the J48, and the Random Forest algorithms. Map reduction is a programming model for simultaneous processing and generation of massive amounts of data. We examine Map Reduce employing Naive Bayes, J48, and Random Forest algorithms with a variety of processing features for efficient mining that also saves energy. The Naive Bayes algorithm generates more energy and fewer maps. Priority-based scheduling is a task that allocates schedules based on the jobs’ requirements and utilization. As a result of decreasing the maps, the system’s workload is reduced, and energy efficiency is improved. The experimental comparison of the several algorithm techniques (Naive Bayes, J48, and Random Forest) have applied in this article and found that the Random forest is performed better than remaining two algorithms i.e. 92%.
01 Mar 2023
TL;DR: In this article , different classification methods are used for lung cancer prediction, such as Bayesian Network, Logistic Regression, J48, Random Forest and Naive Bayes.
Abstract: People who have never smoked can get lung cancer, but smokers have a higher risk than non-smokers. Any aspect of the respiratory system can be affected by lung cancer, which can start anywhere in the lungs, Different classification methods are used for lung cancer prediction. This article uses five different classification algorithms to predict lung cancer in patients using Kaggle dataset. Bayesian Network, Logistic Regression, J48, Random Forest and Naive Bayes methods are used, Based on the carefully identified correct and incorrect cases, the quality of the result was measured using the evaluation technique and the WEKA tool. The experimental results showed that Logistic Regression performed best (91.90 % ), followed by Naive Bayes (90.29 % ), Bayesian Network (88.34 % ), j48 (86.08 % ) and Random Forest (90.93 % ).
TL;DR: In this paper, the authors have used statistical, discrete wavelet and empirical mode decomposition for feature extraction process and J48 decision tree for feature selection for bearing failure detection using K*, Random Forest and support vector machine algorithm.
Abstract: Ball bearing failure are most common failure in rotating machinery, which can be catastrophic. Hence obtaining early failure warning along with precise fault detection technique is at most important. Early detection and timely intervention are the key in condition monitoring for long term endurance of machine components. The early research has used signal processing and spectral analysis extensively for fault detection however data mining with machine learning is most effective in fault diagnosis, the same is presented in this paper. The vibration signals are acquired for an output shaft antifriction bearing in a two-wheeler gearbox operated at various loading conditions with healthy and fault conditions. Data mining is employed for these acquired signals. Statistical, discrete wavelet and empirical mode decomposition are employed for feature extraction process and J48 decision tree for feature selection. Classification is carried out using K*, Random forest and support vector machine algorithm. The classifiers are trained and tested using tenfold cross validation method to diagnose the bearing fault. A comparative study of feature extraction and classifiers are done to evaluate the classification accuracy. The results obtained from K* classifier with wavelet feature yielded better accuracy than rest other classifiers with classification accuracy 92.5% for bearing fault diagnosis.