A Genetic-Fuzzy Approach for Automatic Text Categorization

Mane, None Alka A. Kumbhar, P. B. Kumbharkar, D. T.; Mali, Manisha; Atique, Mohammad

doi:10.1109/iacc.2017.0123

Cited by 4 publications

(12 citation statements)

References 21 publications

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“…Romero et al [74] have classified text data by using FR and found that the accuracy of the FR-based classification model was 81%. Kumbhar, Mali and Atique [45] have indicated that FR classification accuracy of 80.99% is increased to 87.8% by supporting genetic algorithms. Besides, they found that this increase was directly related to the number of fuzzy rules.…”

Section: Related Work On Classification Of Short Textmentioning

confidence: 89%

Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce

Fidan

2020

J. theor. appl. electron. commer. res.

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Companies have gained important advantages by the development of electronic commerce. Consumer evaluations in electronic environment offer great possibilities for analysis. The fact that the consumer opinions are comprised of textual data, analyzes have complicated and challenging process. In recent years, it is seen that text mining methods are used in analyzes in the literature. However, the evaluations of consumers which are formed by short texts make it necessary to realize the analysis with insufficient data. The weighting methods such as Term Frequency and Term Frequency-Inverse Document Frequency as well as common used classification algorithms such as Naïve Bayes and Support Vector Machine have some inadequacies in short text analysis. In this study, a grey relational classification model based on Vector Space Model and Bag of Words has been developed. The model was first applied to the positive-negative categorization of the evaluations, then, applied to the classification of negative evaluations. It was determined that the accuracy level of the model is higher than the classification algorithms commonly used in short text. According to the results of the research, 9637 negative evaluations in 24479 consumer opinion were determined, and 50.4% of the negative evaluations were found to have the most problems related to product.

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Section: Related Work On Classification Of Short Textmentioning

confidence: 89%

Grey Relational Classification of Consumers' Textual Evaluations in E-Commerce

Fidan

2020

J. theor. appl. electron. commer. res.

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“…A suitable metaheuristic method was adapted in fifteen studies (15) to accomplish the FS problem in TC, relying on its mechanism of finding a new subset of the relevant features to tackle the massive number of features in the original text data. Bidi, and Elberrichi [37], and Kumbhar et al [20] used Genetic Algorithm (GA) as an FS method to improve the text classification. In [37], a text representation method (i.e., bag of words (BOW), N-gram, stemming, and conceptual representation) are used prior to the selection of the optimal number of the features subsets, and, in contrary, in [20], they did not use any text representation methods.…”

Section: A: Adapted Metaheuristic Methodsmentioning

confidence: 99%

“…Bidi, and Elberrichi [37], and Kumbhar et al [20] used Genetic Algorithm (GA) as an FS method to improve the text classification. In [37], a text representation method (i.e., bag of words (BOW), N-gram, stemming, and conceptual representation) are used prior to the selection of the optimal number of the features subsets, and, in contrary, in [20], they did not use any text representation methods. Using the GA method, both researchers used a classification method based on the optimally selected number of feature subsets.…”

Section: A: Adapted Metaheuristic Methodsmentioning

confidence: 99%

“…Twenty (20) papers out of thirty-seven (37) papers propose metaheuristic optimization algorithms or a combination of algorithms (i.e., adapted, modified, and hybridized algorithms) used for FS, which is known as the wrapper method [1], [2], [11], [20], [37]- [52]. It typically uses an evaluation function to select the feature subset in tandem with the classifier algorithm process.…”

Section: ) Wrapper-based Fs Approachesmentioning

confidence: 99%

“…Because the optimization algorithm uses an objective function to evaluate the consistent features, taking into account the classification accuracy and the number of selected features. The examples of the wrapper-based approach are recursive feature elimination, sequential (forward /backward) feature selection (SFS) algorithms, and genetic algorithm (GA) [20], [21]. Besides, apart from the wrapper and embedded-based approaches that used the classification method in their mechanism to measure the performance of TC, the wrapper-based uses feature dependencies.…”

Section: Introductionmentioning

confidence: 99%

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Wrapper and Hybrid Feature Selection Methods Using Metaheuristic Algorithms for English Text Classification: A Systematic Review

et al. 2022

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Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematically studied by exploring available studies of different metaheuristic algorithms used for FS to improve TC. This paper will contribute to the body of existing knowledge by answering four research questions (RQs): 1) What are the different approaches of FS that apply metaheuristic algorithms to improve TC? 2) Does applying metaheuristic algorithms for TC lead to better accuracy than the typical FS methods? 3) How effective are the modified, hybridized metaheuristic algorithms for text FS problems?, and 4) What are the gaps in the current studies and their future directions? These RQs led to a study of recent works on metaheuristic-based FS methods, their contributions, and limitations. Hence, a final list of thirty-seven (37) related articles was extracted and investigated to align with our RQs to generate new knowledge in the domain of study. Most of the conducted papers focused on addressing the TC in tandem with metaheuristic algorithms based on the wrapper and hybrid FS approaches. Future research should focus on using a hybrid-based FS approach as it intuitively handles complex optimization problems and potentiality provide new research opportunities in this rapidly developing field.

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