There is significant interest in the network management and industrial security community about the need to identify the ‘‘best’’ and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order ...

Text Categorization (TC) is the process of grouping texts into one or more predefined categories based on their content. It has become a key technique for handling and organizing text data. One of the most important issues in TC is Feature Selection (FS). Many FS methods have been put forward and widely used in TC field, such as Information Gain (IG), Document Frequency thresholding (DF) and Mu...

In reinforcement learning, the state of the real world is often represented by feature vectors. However, not all of the features may be pertinent for solving the current task. We propose Feature Selection Explore and Exploit (FS-EE), an algorithm that automatically selects the necessary features while learning a Factored Markov Decision Process, and prove that under mild assumptions, its sample...

A hybrid feature selection (HFS) algorithm to obtain the optimal set attain forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic (EGA) and random forest method, which embedded online (FS). Using selected features, performance of forecaster was tested signify utility methodology. For this, a day-ahead STLF using M5P (a co...

The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), crucial preprocessing step in that seeks out ideal set characteristics with maximum accuracy possible. Due to their dominance over traditional optimization techniques, researchers concentrating on variety metaheuristic (or evolutionary) algorithms trying suggest cutting-edge h...

Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been enhance the efficiency of FS algorithms. These approaches processing time and improve accuracy learning models. In this paper, a developed method called MPAO based on marine predators algorithm (MPA) “narrowed exploration” strategy Aquila optimizer (AO) proposed hand...

Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fu...

In this paper we describe a preliminary study into the use of feature selection in incremental hierarchical clustering. Our aim is to add this capability to the clustering system, still maintaining the in-cremental nature of the learning process. This constraint lead us to consider a dynamic feature selection mechanism which is performed parallel to the clustering process. In addition, feature ...

Feature Selection (FS) is a major preprocessing stage which aims to improve Machine Learning (ML) models’ performance by choosing salient features, while reducing the computational cost. Several approaches are presented select most Optimal Features Subset (OFS) in given dataset. In this paper, we introduce an FS-based approach named Reptile Search Algorithm–Snake Optimizer (RSA-SO) that employs...