An application of MOGW optimization for feature selection in text classification

Asgarnezhad, Razieh; Monadjemi, S. Amirhassan; Aghaei, Mohammadreza Soltan

doi:10.1007/s11227-020-03490-w

Cited by 19 publications

(9 citation statements)

References 36 publications

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“…In addition to the classic particle swarm optimization [17][18][19] and genetic algorithm [20], some other novel bionic algorithms have been successfully applied to text feature selection. For example, the cat swarm optimization algorithm [21], artificial fish swarm algorithm [22], the Jaya optimization algorithm [23], the firefly algorithm [24],the grey wolf optimization algorithm [25]and the ant colony algorithm [26]. To solve the problem of Arabic text classification, Chantar et al proposed an enhanced binary gray wolf optimizer (GWO) as a feature selection method [27].…”

Section: Related Workmentioning

confidence: 99%

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Chen

Zhou

Shi

2022

Int J Comput Intell Syst

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Text classification is one of the important technologies in the field of text data mining. Feature selection, as a key step in processing text classification tasks, is used to process high-dimensional feature sets, which directly affects the final classification performance. At present, the most widely used text feature selection methods in academia are to calculate the importance of each feature for classification through an evaluation function, and then select the most important feature subsets that meet the quantitative requirements in turn. However, ignoring the correlation between the features and the effect of their mutual combination in this way may not guarantee the best classification effect. Therefore, this paper proposes a chaotic antlion feature selection algorithm (CAFSA) to solve this problem. The main contributions include: (1) Propose a chaotic antlion algorithm (CAA) based on quasi-opposition learning mechanism and chaos strategy, and compare it with the other four algorithms on 11 benchmark functions. The algorithm has achieved a higher convergence speed and the highest optimization accuracy. (2) Study the performance of CAFSA using CAA for feature selection when using different learning models, including decision tree, Naive Bayes, and SVM classifier. (3) The performance of CAFSA is compared with that of eight other feature selection methods on three Chinese datasets. The experimental results show that using CAFSA can reduce the number of features and improve the classification accuracy of the classifier, which has a better classification effect than other feature selection methods.

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Section: Related Workmentioning

confidence: 99%

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Chen

Zhou

Shi

2022

Int J Comput Intell Syst

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“…The results of the proposed approaches outperformed GA and PSO. Another discrete version of GWO, the multi-objective gray wolf optimization algorithm, was presented in [36]. This proposed algorithm's goal was to reduce the dimensions of the features for multiclass sentiment classification.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Heap Based Optimizer Algorithm for Feature Selection

Ali

P.²,

Elminaam³

2022

Mathematics

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The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer B_HBO are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer B_HBO are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that B_HBO is superior or equivalent to the other algorithms used in the literature.

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“…These approaches are the ML, Lexicon-Based, 15 Hybrid, Ensemble, 16 and Heuristic. 17 The ML approaches are applied in supervised, unsupervised, and semi-supervised methods by adopting linguistic features. The lexicon-based approaches are divided into corpus-based and dictionary-based approaches; with the advantage in determining the domain and the context-specific opinion words through a domain corpus.…”

Section: Sc Approachesmentioning

confidence: 99%

“…The four objectives or worth the KNN classifier error, NB, SVM, and Perceptron are obtained for each element of the initial population to this framework. The value of element i of these four objectives is expressed as follows in Equation (17).…”

Section: Evaluating the Population Elementsmentioning

confidence: 99%

A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

Asgarnezhad

Monadjemi

Aghaei

2021

Concurrency and Computation

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Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user-generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time-consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation-Power concept, a multi-objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi-objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied; which compared with the two methods to measure accuracy, precision, recall, and error-index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f-measure.

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An application of MOGW optimization for feature selection in text classification

Cited by 19 publications

References 36 publications

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

An Efficient Heap Based Optimizer Algorithm for Feature Selection

A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

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