Abstract
With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.
Highlights
The huge increase of data volume results in different challenges and problems such as irrelevant, high dimensionality, and noisy data [1]
feature selection (FS) methods have been widely employed in different fields, such as human action detection [4], text classification [5], COVID-19 CT images classification [6], neuromuscular disorders [7], data analytics problems [8], parameter estimation of biochemical systems [9], MR image segmentation [10,11], and other applications [12,13,14]
We propose a new FS approach using a new variant of the electric fish optimization (EFO)
Summary
The huge increase of data volume results in different challenges and problems such as irrelevant, high dimensionality, and noisy data [1]. Such problems affect the efficiency and accuracy of the machine learning algorithms and lead to high computational costs. FS methods are generally used to capture data properties by selecting a subset of relevant features [3]. They removed unnecessary and noisy data [3]. Where xij is the position number j in the solution number I, max, and min are the maximum and minimum boundaries, respectively. The frequency value is given between the maximum and minimum of the fitness function values: ! t t f it −
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