A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Abstract

Specialized data preparation techniques, ranging from data cleaning, outlier detection, missing value imputation, feature selection (FS), amongst others, are procedures required to get the most out of data and, consequently, get the optimal performance of predictive models for classification tasks. FS is a vital and indispensable technique that enables the model to perform faster, eliminate noisy data, remove redundancy, reduce overfitting, improve precision and increase generalization on testing data. While conventional FS techniques have been leveraged for classification tasks in the past few decades, they fail to optimally reduce the high dimensionality of the feature space of texts, thus breeding inefficient predictive models. Emerging technologies such as the metaheuristics and hyper-heuristics optimization methods provide a new paradigm for FS due to their efficiency in improving the accuracy of classification, computational demands, storage, as well as functioning seamlessly in solving complex optimization problems with less time. However, little details are known on best practices for case-to-case usage of emerging FS methods. The literature continues to be engulfed with clear and unclear findings in leveraging effective methods, which, if not performed accurately, alters precision, real-world-use feasibility, and the predictive model's overall performance. This paper reviews the present state of FS with respect to metaheuristics and hyper-heuristic methods. Through a systematic literature review of over 200 articles, we set out the most recent findings and trends to enlighten analysts, practitioners and researchers in the field of data analytics seeking clarity in understanding and implementing effective FS optimization methods for improved text classification tasks.

Multiclass feature selection with metaheuristic optimization algorithms: a review

“…Similarly, Rostami et al [203] introduced a comparative analysis of other swarm intelligencebased feature selection methods, considering the strength and weaknesses of this method. Moreover, Abiodun et al [5] conducted an organized review of evolving feature selection methods for text classification optimization tasks. The scope of their work was from 2015 to 2021, reviewing over 200 articles concerning metaheuristic and hyperheuristic procedures.…”

A deeper look into cybersecurity issues in the wake of Covid-19: A survey

“… Andrade et al, 2020 , De Mello et al, 2019 , Wang et al, 2010 , Gourinchas et al, 2020 , Anderson et al, 2020 , Kalemli-Ozcan et al, 2020 , Ibn-Mohammed et al, 2021 , Abiodun et al, 2021a , Yadav, 2021 , Ahmed and Tushar, 2020 , Omolara et al, 2018a , Omolara et al, 2022 , Netherlands, 2020 , Omolara et al, 2019a , Esther Omolara et al, 2020 , Oludare et al, 2018a , Oludare et al, 2018b , UNESCO:, 2020 , Midkiff and DaSilva, 2000 , Google, 2020 , Prates et al, 2020 , Suhono et al, 2020 , Omolara et al, 2019b , BBCnews (April, 13, 2020 , Abiodun et al, 2021b , Omolara et al, 2018b , Omolara et al, 2019c , Wilson, 2014 , Masciandaro and (Ed.). , 2017 .…”

Study on the evolution of Chinese characters based on few-shot learning: From oracle bone inscriptions to regular script

“…Subsequently, the VGG16 network [35] explores the relationship between the depth and performance of convolutional neural networks, which has strong extensibility; the Inception network [36] differs from the previous two networks in that it adds a structure called Inception, the main advantage of which is the reduction of training parameters; the ResNet network [37] introduces residual network structure, and it can realize that the accuracy does not decrease with the network deepening. These classic neural networks and other network structure models continue to emerge, resulting in the application of neural networks in various fields, such as natural language processing [38] and information decryption [39,40], which bring great convenience to human life.…”