BFRA: A New Binary Hyper-Heuristics Feature Ranks Algorithm for Feature Selection in High-Dimensional Classification Data

Abstract

Feature selection is one of the main issues in machine learning algorithms. In this paper, a new binary hyper-heuristics feature ranks algorithm is designed to solve the feature selection problem in high-dimensional classification data called the BFRA algorithm. The initial strong population generation is done by ranking the features based on the initial Laplacian Score (ILR) method. A new operator called AHWF removes the zero-importance or redundant features from the population-based solutions. Another new operator, AHBF, selects the key features in population-based solutions. These two operators are designed to increase the exploitation of the BFRA algorithm. To ensure exploration, we introduced a new operator called BOM, a binary counter-mutation that increases the exploration and escape from the BFRA algorithm’s local trap. Finally, the BFRA algorithm was evaluated on 26 high-dimensional data with different statistical criteria. The BFRA algorithm has been tested with various meta-heuristic algorithms. The experiments’ different dimensions show that the BFRA algorithm works like a robust meta-heuristic algorithm in low dimensions. Nevertheless, by increasing the dataset dimensions, the BFRA performs better than other algorithms in terms of the best fitness function value, accuracy of the classifiers, and the number of selected features compared to different algorithms. However, a case study of sentiment analysis of movie viewers using BFRA proves that BFRA algorithms demonstrate affordable performance.

An Improved hybrid Salp Swarm Optimization and African Vulture Optimization Algorithm for Global Optimization Problems and Its Applications in Stock Market Prediction

“…The outcomes of functions with fixed dimensions are presented in Table (7). The results obtained from the tests of fixed dimension functions are shown in Table (7). The AVOA-SSA algorithm performed excellently in all functions except the F20 function, according to the results displayed.…”

“…The AVOA-SSA algorithm had an excellent performance in the NIKKEI index. Figure (7) shows that the AVOA-SSA algorithm worked well in the early epochs and correctly estimated almost all the areas. It can be seen even in the range of 380 to 410.…”

“…This good performance indicates the superb ability of the AVOA-SSA algorithm in both the exploration and exploitation phases. The outcomes of functions with fixed dimensions are presented in Table (7). The results obtained from the tests of fixed dimension functions are shown in Table (7).…”

“…Solving large-scale problems using exact algorithms requires a lot of time and resources, which is practically impossible [6]. Stochastic algorithms can obtain optimal or near-optimal solutions in an acceptable time [7]. Unfortunately, meta-heuristic algorithms have disadvantages such as early convergence and getting stuck in local optima.…”

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

“…It combines precision and recall into a single value and is particularly useful for balancing these metrics. This metric has been used in [22,33,35,39,45,48,51,55,59,60,83,92,98,101,117,132,141,149,155,162,164,167,171,[176][177][178]182,184,185] and mathematically is defined as follows:…”