In this study, we propose a new feature (or gene) selection algorithm using Bayes classification approach. The algorithm can find gene subset crucial for cancer classification problem. Problem statement: Gene identification plays important role in human cancer classification problem. Several feature selection algorithms have been proposed for analyzing and understanding influential genes using ...

In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS) algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solvin...

The work is concerned with the use of Ant Colony Optimization algorithm for feature selection of Keystrokes Dynamics and comparison of classification accuracy of Multi-SVM and KNN classifiers. There are various approaches used for feature subset selection but, ACO algorithm gives good performance than other feature selection algorithm like Genetic Based algorithm and Particle Swarm Optimization...

Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistan...

This paper proposes a new Genetic Algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS). This GA performs a global search in the space of candidate feature subsets, in order to select a high-quality feature subset that is used by a multi-label classification algorithm – in this work, the Multi-Label k-NN algorithm. We compare the results of GA-ML-CFS with the results of the ...

Gene selection is usually the crucial first step in microarray data analysis. One class of typical approaches is to calculate some discriminative scores using data associated with a single gene. Such discriminative scores are then sorted and top ranked genes are selected for further analysis. However, such an approach will result in redundant gene set since it ignores the complex relationships ...

High-dimensional intrusion detection data concentration information redundancy results in lower processing velocity of intrusion detection algorithm. Accordingly, the current study proposes an intrusion feature selection algorithm based on particle swarm optimization (PSO). Analyzing the features of the relevance between network intrusion data allows the PSO algorithm to optimally search in a f...