Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

Al-Tashi, Qasem; Abdulkadir, Said Jadid; Rais, Helmi Md; Mirjalili, Seyedali; Alhussian, Hitham

doi:10.1109/access.2020.3007291

Cited by 104 publications

(41 citation statements)

References 90 publications

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“…To capture the essential but hidden variables for the significant insight is an endeavor task, but it is the best choice for best decisions and predictions. Most of the data scientists [30], [31] are trying to find the best methods to achieve the best features insights. In this research, the WA is used to select the feature sets (named as premier feature sets) across the given dataset.…”

Section: Proposed Methods -Gawamentioning

confidence: 99%

“…Feature selection is deliberated as a vital part of many machine learning methods that remarkably affect the model accuracy. Generally, it has two contractional aims, maximizing the classification's performance and minimizing the feature size [30], [31]. Several feature selection techniques have been introduced, like Mutual Information (MI), Term Frequency -Inverse Document Frequency (TF-IDF), Information Gain (IG), and Chi-Square (CS) for the sentiment classification with a different machine learning algorithm.…”

Section: Related Workmentioning

confidence: 99%

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GAWA–A Feature Selection Method for Hybrid Sentiment Classification

et al. 2020

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Sentiment analysis or opinion mining is the key to natural language processing for the extraction of useful information from the text documents of numerous sources. Several different techniques, i.e., simple rule-based to lexicon-based and more sophisticated machine learning algorithms, have been widely used with different classifiers to get the factual analysis of sentiment. However, lexicon-based sentiment classification is still suffering from low accuracies, mainly due to the deficiency of domain-oriented competitive dictionaries. Similarly, machine learning-based sentiment is also tackling the accuracy constraints because of feature ambiguity from social data. One of the best ways to deal with the accuracy issue is to select the best feature-set and reduce the volume of the feature. This paper proposes a method (namely, GAWA) for feature selection by utilizing the Wrapper Approaches (WA) to select the premier features and the Genetic Algorithm (GA) to reduce the size of the premier features. The novelty of this work is the modified fitness function of heuristic GA to compute the optimal features by reducing the redundancy for better accuracy. This work aims to present a comprehensive model of hybrid sentiment by using the proposed method, GAWA. It will be valued in developing a new approach for the selection of feature-set with a better accuracy level. The experiments revealed that these techniques could reduce the feature-set upto 61.95% without negotiating the accuracy level. The new optimal feature sets enhanced the efficiency of the Naïve Bayes algorithm up to 92%. This work is compared with the conventional method of feature selection and concluded the 11% better accuracy than PCA and 8% better than PSO. Furthermore, the results are compared with the literature work and found that the proposed method outperformed the previous research.

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Section: Proposed Methods -Gawamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

et al. 2020

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“…Carefully understanding the dataset along with dimensionality reduction issues before any data analysis process are crucial to the success of the analysis itself [32]. Data preprocessing involves transforming raw data into a format that is suitable for processing.…”

Section: Feature Selection Algorithmmentioning

confidence: 99%

SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

et al. 2021

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The Dimensionality Curse is one of the most critical issues that are hindering faster evolution in several fields broadly, and in bioinformatics distinctively. To counter this curse, a conglomerate solution is needed. Among the renowned techniques that proved efficacy, the scaling-based dimensionality reduction techniques are the most prevalent. To insure improved performance and productivity, horizontal scaling functions are combined with Particle Swarm Optimization (PSO) based computational techniques. Optimization algorithms are an interesting substitute to traditional feature selection methods that are both efficient and relatively easier to scale. Particle Swarm Optimization (PSO) is an iterative search algorithm that has proved to achieve excellent results for feature selection problems. In this paper, a composite Spark Distributed approach to feature selection that combines an integrative feature selection algorithm using Binary Particle Swarm Optimization (BPSO) with Particle Swarm Optimization (PSO) algorithm for cancer prognosis is proposed; hence Spark Distributed Particle Swarm Optimization (SDPSO) approach. The effectiveness of the proposed approach is demonstrated using five benchmark genomic datasets as well as a comparative study with four state of the art methods. Compared with the four methods, the proposed approach yields the best in average of purity ranging from 0.78 to 0.97 and F-measure ranging from 0.75 to 0.96.

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“…This can be done using a heuristic strategy which performs a guided search over the entire solution space to find a reasonably good feature subset which may not be the optimal solution but is acceptable within computational constraints. Higher-level heuristics or meta-heuristics have become quite popular in recent years to solve FS problems in different fields [8] such as handwriting recognition [9], benchmark problems [10], gene selection [11], medical diagnosis [12], financial problems, network intrusion and security .…”

Section: A Feature Selectionmentioning

confidence: 99%

“…of f spring1 = r of * male + (1 − r of ) * f emale (8) of f spring2 = r of * f emale + (1 − r of ) * male (9)…”

Section: Some Preliminaries a Mayfly Optimization Algorithmmentioning

confidence: 99%

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

et al. 2020

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Feature selection is a process to reduce the dimension of a dataset by removing redundant features, and to use the optimal subset of features for machine learning or data mining algorithms. This helps to minimize the time requirement to train a learning algorithm as well as to lessen the storage requirement by ignoring the less-informative features. Feature selection can be considered as a combinatorial optimization problem. In this paper, the authors have presented a new feature selection algorithm called Mayfly-Harmony Search (MA-HS) based on two meta-heuristics namely Mayfly Algorithm and Harmony Search. Mayfly Algorithm has not hitherto been used for feature selection problems to the best of the author's knowledge. An S-shaped transfer function is incorporated for converting it into a binary version of Mayfly Algorithm. When different candidate solutions obtained from various regions of the search space using Mayfly Algorithm are taken into the harmony memory and processed by Harmony Search, a superior solution can be ensured. This is the primary reason for proposing a hybrid of Mayfly Algorithm and Harmony Search. Thus, combining harmony search with Mayfly Algorithm leads to an increased exploitation of the search space and an overall improvement in the performance of Mayfly-Harmony Search (MA-HS) algorithm. The proposed algorithm has been applied on 18 UCI datasets and compared with 12 other state-of-the-art meta-heuristic FS methods. Experiments have also been performed on three high-dimensional microarray datasets. The results obtained support the superior performance of the algorithm compared to the other methods. The source code of the proposed algorithm can be found using the link as follows: https://github.com/trin07/MA-HS.

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Approaches to Multi-Objective Feature Selection: A Systematic Literature Review

Cited by 104 publications

References 90 publications

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm

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