Feature selection is an important tool reducing necessary feature acquisition time in some applications. Standard methods, proposed in the literature, do not cope with the measurement cost issue. Including the measurement cost into the feature selection process is difficult when features are grouped together due to the implementation. If one feature from a group is requested, all others are ava...

Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this is...

Iterative search margin based algorithm(Simba) has been proven effective for feature selection. However, it still has the following disadvantages: (1) the previously proposed model still lacks enough robust to noises; and (2) the given model does not use any global information, in this way some useful discrimination information may be lost and the convergence speed is also influenced in some ca...

Intrusion detection systems are designed to provide security in computer networks, so that if the attacker crosses other security devices, they can detect and prevent the attack process. One of the most essential challenges in designing these systems is the so called curse of dimensionality. Therefore, in order to obtain satisfactory performance in these systems we have to take advantage of app...

This paper presents a machine learning model using random forest (RF) algorithm with the recursive feature elimination (RFE) for soil liquefaction prediction. The prediction is tested on 253 CPT-based field data from different earthquakes. RFE, which one of selection methods, was adopted eliminating irrelevant features in mentioned dataset, and then performance RFE-RF (i.e., determined by RFE m...

In classification, feature selection is an important, but difficult problem. Particle swarm optimisation (PSO) is an efficient evolutionary computation technique. However, the traditional personal best and global best updating mechanism in PSO limits its performance for feature selection and the potential of PSO for feature selection has not been fully investigated. This paper proposes a new in...

Introduction: The early diagnosis of breast cancer as prevalent cancer among women, is a necessity in the research on cancers since it could simplify the clinical management of other patients. The importance of the classification of breast cancer patients into high- or low-risk groups has led research groups in the biomedical and informatics departments to evaluate and use computer techniques s...