Anomaly detection from Big Cybersecurity Datasets is very important; however, this a challenging and computationally expensive task. Feature selection (FS) an approach to remove irrelevant redundant features select subset of features, which can improve the machine learning algorithms’ performance. In fact, FS effective preprocessing step anomaly techniques. This article’s main objective quantif...

Feature selection (FS) is an important research topic in machine learning. Usually, FS modelled as a bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One the main issues real-world applications missing data. Databases with data are likely to be unreliable. Thus, performed on set some also In order directly control this issue plaguing fie...

Feature selection study is gaining importance due to its contribution to save classification cost in terms of time and computation load. In search of essential features, one of the methods to search the features is via the decision tree. Decision tree act as an intermediate feature space inducer in order to choose essential features. In decision tree-based feature selection, some studies used d...

In most gene expression data, the number of training samples is very small compared to the large number of genes involved in the experiments. However, among the large amount of genes, only a small fraction is effective for performing a certain task. Furthermore, a small subset of genes is desirable in developing gene expression based diagnostic tools for delivering reliable and understandable r...

Feature Selection (FS) is a process which attempts to select features which are more informative. It is an important step in knowledge discovery from data. Conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining...

This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on concept size of given objects and/or clusters described using fixed number equal probability bin-rectangles. In each step clustering, we agglomerate so as to minimize compactness generated cluster. means that plays role simila...

In many fields such as signal processing, machine learning, pattern recognition and data mining, it is common practice to process datasets containing huge numbers of features. cases, Feature Selection (FS) often involved. Meanwhile, owing their excellent global search ability, evolutionary computation techniques have been widely employed the FS. So, a powerful method calculation fast than other...

This paper proposes a γ-Artificial Bee Colony – Feature Selection (γ-ABC-FS) approach to identify the salient feature subset that improves classification accuracy. γ-ABC-FS hybridizes Artificial Colony(ABC) algorithm by employing Rough Set Theory concepts in ABC search process and improved initialization local strategies. The proposed is intended begin evolutionary not excluding attributes cont...

Classification tasks often include, among the large number of features to be processed in datasets, many irrelevant and redundant ones, which can even decrease efficiency classifiers. Feature Selection (FS) is most common preprocessing technique utilized overcome drawbacks high dimensionality datasets has two conflicting objectives: The first function aims maximize classification performance or...