background: myocardial infarction (mi) occurs due to heart muscle death that costs like human life, which is higher than the treatment costs. this study aimed to present an mi prediction model using classification data mining methods, which consider the imbalance nature of the problem. methods: we enrolled 455 healthy and 295 myocardial infarction cases of visitors to shahid madani specialized ...

This short paper compares the performance of three popular decision tree algorithms: C4.5, C5.0, and WEKA’s J48. These decision tree algorithms are all related in that C5.0 is an updated commercial version of C4.5 and J48 is an implementation of the C4.5 algorithm under the WEKA data mining platform. The purpose of this paper is to verify the explicit or implied performance claims for these alg...

The primary intent of this paper is detect malicious traffic at the network level. To this end, we apply several machine learning techniques to build classifiers that fingerprint maliciousness on IP traffic. As such, J48, Naı̈ve Bayesian, SVM and Boosting algorithms are used to classify malware communications that are generated from dynamic malware analysis framework. The generated traffic log f...

Software risk prediction is the most sensitive and crucial activity of Development Life Cycle (SDLC). It may lead to success or failure a project. The should be predicted earlier make software project successful. A model proposed for requirement risks using dataset machine learning techniques. In addition, comparison made between multiple classifiers that are K-Nearest Neighbour (KNN), Average ...

Classification is an important data mining technique with broad applications. It classifies data of various kinds. Classification is used in every field of our life. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. This paper has been carried out to make a performance evaluation of Naïve Bayes and j48 classification algorithm. Naive ...

Most decision-tree induction algorithms are using a local greedy strategy, where a leaf is always split on the best attribute according to a given attribute selection criterion. A more accurate model could possibly be found by looking ahead for alternative subtrees. However, some researchers argue that the look-ahead should not be used due to a negative effect (called ―decision tree pathology‖)...

Biomarker discovery using mass spectrometry (MS) data is very useful in disease detection and drug discovery. The process of biomarker discovery in MS data must start with feature selection as the number of features in MS data is extremely large (e.g. thousands) while the number of samples is comparatively small. In this study, we propose the use of genetic programming (GP) for automatic featur...

BACKGROUND Myocardial infarction (MI) occurs due to heart muscle death that costs like human life, which is higher than the treatment costs. This study aimed to present an MI prediction model using classification data mining methods, which consider the imbalance nature of the problem. METHODS We enrolled 455 healthy and 295 myocardial infarction cases of visitors to Shahid Madani Specialized ...

In this work, popular discretization techniques for continuous features in data sets are surveyed, and a new one based on equal width binning and error minimization is introduced. This discretization technique is implemented for the UCI Machine Learning Repository [7] dataset, Adult database and tested on two classifiers from WEKA tool [6], NaiveBayes and J48. Relative performance changes for t...

This research aimed on prediction of customer loyalty (Non loyal or Loyal) using the application of data mining in microfinance that helps to build a classification model which supports during loan decision making in the organization. In this study a classification model is built based on the loan data obtained from Joshua Multi Purpose Limited Liability Cooperative (JMPLLC). Experiments using ...