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...

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...

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 ...

In this paper different classification techniques of Data Mining are compared using diverse datasets from University of California, Irvine(UCI). Accuracy and time required for execution by each technique is observed. The Data Mining refers to extracting or mining knowledge from huge volume of data. Classification is an important data mining technique with broad applications. It classifies data ...

Internet growth has increased rapidly due to which number of network attacks have been increased. This emphasis importance of network intrusion detection systems (IDS) for securing the network. It is the process of monitoring and analyzing network traffic for detecting security violations many researcher suggested data mining technique such as classification, clustering ,pattern matching and ru...

Data mining is the upcoming research area to solve various problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 (which is java implementation of C4. 5 algorithm) and multilayer perceptron alias MLP (which is a modification of the standard linear perceptron) of the Weka interface. It can be used for testing severa...