The explosion of the high-dimensional dataset in scientific repository has been encouraging interdisciplinary research on data mining, pattern recognition and bioinformatics. fundamental problem individual Feature Selection (FS) method is extracting informative features for classification model to seek malignant disease at low computational cost. In addition, existing FS approaches overlook fact that a given cardinality, there can be several subsets with similar information. This paper...

In the context of optimal subset selection, hybrid feature selection approaches play a significant role that has been topic substantial number studies because growing need for data mining applications. (FSS) problem; there are two shortcomi ngs to be addressed: At first, finding suitable filter method can reasonably fast and energetically computed large volume data, second, an efficient wrapper strategy discriminate samples over entire search space in considerable amount time. After study...

Metaheuristic algorithms have probable to solve global optimization problems in various fields of engineering and industry. To find a solution by exploring irregular or non-linear surfaces, classical techniques are not able. overcome the limitation approach, recent study, large number metaheuristic methods been investigated improve quality concerning convergence accuracy on complex problems. Nowadays, popular algorithm is introduced called teaching–learning-based (TLBO). It recently being...

The duplicate and insignificant features present in the data set to cause a long-term problem classification of network or web traffic. not only decrease performance but also prevent classifier from making accurate decisions, exclusively when substantial volumes are managed. In this article, author introduced an ensemble feature selection (EFS) technique, where multiple homogeneous (FS) methods combined choose optimal subset relevant non-redundant features. An intrusion detection system,...

Wireless sensor networks (WSNs) play a vital role in present-day world, which are being used different types of applications and occupy an important part networking domain. The main objective WSNs is to sense collect the information from given area interest provide gathered data sink. WSN comprises number nodes with batteries limited energy for communication computational activities, not possible recharge after their deployment region interest. Therefore, saving battery utilising power...

Nature-inspired algorithms as problem-solving methodologies are extremely effective in discovery of optimized solutions multi-dimensional and multi-modal problems. Because qualities like “self-optimization”, “flexibility” etc., nature-inspired for problem solving effectively optimal. Feature selection is an approach to find approximate optimal subset the features which more relevant towards particular outcome. In this study, we focused on how feature may improve credit scoring model’s...

Abstract Nowadays, microarray gene expression data plays a vital role in tumor classification. However, due to the accessibility of limited number tissues compared large genes genomic data, various existing methods have failed identify small subset discriminative genes. To overcome this limitation, paper, we developed new hybrid technique for selection, called ensemble multipopulation adaptive genetic algorithm (EMPAGA) that can overlook irrelevant and classify cancer accurately. The...

Abstract Feature selection is an essential task to predict clinical risk and biomarkers from the gene expression data. For practical matters, choose significant genes, researchers have been addressed several classical feature problems over past decades for subsequent classification of genomics datasets with large ambient dimensionality but a small number observations. To overcome high overfitting issues, in this paper, we developed new technique by combination minimum redundancy maximum...

In the recent era, evolutionary meta-heuristic algorithms is popular research area in engineering and scientific field. One of intelligent Teaching Learning Based Optimization (TLBO). The basic TLBO algorithm follows isolated learning strategy for t he whole population. This invariable may cause misconception knowledge a specific learner, which makes it unable to deal with different complex situations. For solving non-linear optimization problems, local optimum frequently happens generating...