Feature selection using bare-bones particle swarm optimization with mutual information

Song, Xianfeng; Zhang, Yong; Gong, Dunwei; Sun, Xiaoyan

doi:10.1016/j.patcog.2020.107804

Cited by 181 publications

(69 citation statements)

References 47 publications

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“…The selection of important and suitable identification features can not only simplify the calculation but also allow understand the causal relationship, which is a critical part of machine learning. The advantages of feature selection [39][40][41] include (1) reduce data collection cost, (2) improved data processing through the removal of redundant data and simplification of patterns, resulting in faster computing, (3) improved data interpretation since feature selection improves prediction results, and accelerates model derivation and knowledge discovery. The objective is to find out the most relevant classification features, reduce the dimensions, and correct training samples so as to select important and effective conditional attributes.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Chen

Lin

et al. 2021

Mathematics

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In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

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Section: Feature Selection Methodsmentioning

confidence: 99%

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Chen

Lin

et al. 2021

Mathematics

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“…Song et al [ 33 ] introduced an FS approach based on a new variant of the PSO algorithm, called bare bones PSO. The main idea is to use a swarm initialization technique depending on label correlation.…”

Section: Related Workmentioning

confidence: 99%

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

Ibrahim

Abualigah

Ewees

et al. 2021

Entropy

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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.

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“…All "birds" in the "flock" update their positions continuously to converge on the best position for the optimum solution [39], [40]. Some recent research have applied PSO for feature selection in classification, such as using multiobjective PSO for cost-based feature selection problems in classification [38], improving multi-objective PSO for a multi-label feature selection algorithm [41], studying a filter-based bare-bone PSO algorithm for unsupervised feature selection [42], applying bare-bones PSO with mutual information for feature selection algorithm [43]. PSO has showed as an effective feature selection method that enhance significantly the classification performance in research [44], [45].…”

Section: A Related Workmentioning

confidence: 99%

Combining Binary Particle Swarm Optimization With Support Vector Machine for Enhancing Rice Varieties Classification Accuracy

et al. 2021

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The objective of this study is to classify the rice grains of seventeen different varieties popularly planted in Vietnam. Image processing is used to extract color, morphological, and texture features of the rice grains. Five feature subsets are formed, namely, morphological, basic color, clustering color, statistical, and gray level co-occurrence matrix (GLCM). These subsets and combined sets are evaluated for classification ability with a support vector machine (SVM). A dataset of 248 features, including a total of color, morphological, and texture features classified with the SVM gives an overall accuracy of 88.29%. To decrease the number of used features and to improve the classification accuracy, the proposed method combining binary particle swarm optimization (BPSO) and the SVM, called BPSO+SVM, is applied to the dataset. In the results, classification accuracy from BPSO+SVM reaches 93.94% using only 96 selected features. The obtained result shows the proposed method achieves higher classification accuracy than the SVM alone, and the required number of features is only 39% of the total dataset. This result can be applied for developing an automatic classification and identification system of rice varieties.INDEX TERMS Rice varieties, color features, morphological features, texture features, support vector machine, binary particle swarm optimization.

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Feature selection using bare-bones particle swarm optimization with mutual information

Cited by 181 publications

References 47 publications

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

Combining Binary Particle Swarm Optimization With Support Vector Machine for Enhancing Rice Varieties Classification Accuracy

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