Review of swarm intelligence-based feature selection methods

Rostami, Mehrdad; Berahmand, Kamal; Nasiri, Elahe; Forouzandeh, Saman

doi:10.1016/j.engappai.2021.104210

Cited by 327 publications

(128 citation statements)

References 157 publications

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“…A detailed description of the state of art is given in the following subsection. [57]. Some examples are ant colonies, bee colonies, animal herding, birds flocking, fish schooling, hawks hunting, bacterial growth and microbial intelligence [58].…”

Section: It Takes Into Cognizant the Interaction Of Featuresmentioning

confidence: 99%

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

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

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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.

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Section: It Takes Into Cognizant the Interaction Of Featuresmentioning

confidence: 99%

“…An extensive treatment of the swarm-based feature selection method and its categories can be found in the published article by Rostami et al [57].…”

Section: It Takes Into Cognizant the Interaction Of Featuresmentioning

confidence: 99%

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

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

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“…Relatively new methods called Swarm Intelligence (SI) are gaining popularity in FS as they can solve NP-hard computational problems. Authors of Swarm Intelligence Algorithms for Feature Selection: A Review [1], A survey on swarm intelligence approaches to feature selection in data mining [9] and Review of swarm intelligence-based feature selection methods [10] provide reviews on applying SI for FS. However, we did not find articles comparing different algorithms on the same datasets.…”

Section: Related Workmentioning

confidence: 99%

Comparing Swarm Intelligence Algorithms for Dimension Reduction in Machine Learning

Kicska

Kiss

2021

BDCC

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Nowadays, the high-dimensionality of data causes a variety of problems in machine learning. It is necessary to reduce the feature number by selecting only the most relevant of them. Different approaches called Feature Selection are used for this task. In this paper, we propose a Feature Selection method that uses Swarm Intelligence techniques. Swarm Intelligence algorithms perform optimization by searching for optimal points in the search space. We show the usability of these techniques for solving Feature Selection and compare the performance of five major swarm algorithms: Particle Swarm Optimization, Artificial Bee Colony, Invasive Weed Optimization, Bat Algorithm, and Grey Wolf Optimizer. The accuracy of a decision tree classifier was used to evaluate the algorithms. It turned out that the dimension of the data can be reduced about two times without a loss in accuracy. Moreover, the accuracy increased when abandoning redundant features. Based on our experiments GWO turned out to be the best. It has the highest ranking on different datasets, and its average iteration number to find the best solution is 30.8. ABC obtained the lowest ranking on high-dimensional datasets.

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“…In feature selection, an attempt is made to choose a set of initial features that satisfy two targets simultaneously: the minimum similarity between the selected genes and the maximum relevance of these genes with the target class [43,44]. The main goal of this step is to select appropriate and important feature from original features [45].…”

Section: Feature Selectionmentioning

confidence: 99%

Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

et al. 2021

Self Cite

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Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

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Review of swarm intelligence-based feature selection methods

Cited by 327 publications

References 157 publications

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

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

Comparing Swarm Intelligence Algorithms for Dimension Reduction in Machine Learning

Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

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