A modified multi objective heuristic for effective feature selection in text classification

Thiyagarajan, Devi; Shanthi, N.

doi:10.1007/s10586-017-1150-7

Cited by 12 publications

(12 citation statements)

References 18 publications

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“…In [48], the experimental results prove that the MAFSA as an FS method had overcome the traditional method AFSA concurrently with the most significant subset of features. Meanwhile, several classifiers were adapted (Ada-boost, SVM, and the NB) for TC.…”

Section: Rq2: Does Applying Metaheuristic Algorithms For Tc Lead To B...mentioning

confidence: 81%

“…• Modify/improve: Is to mitigate some of the core issues on the metaheuristic algorithms that suffer from multiplicity, stacking in local optimal space, and other issues. For example, improved performance in obtaining faster convergence and robust global search efficiency could be achieved by balancing exploitation and exploration in the algorithms search space, which in return improves the classification performance [1], [48], [50], [66]. It is for creating a better algorithm that optimizes the solution (feature subsets) to enhance the quality of the initial candidate solutions using the local search strategy.…”

Section: B Discussion Of Findings Based On the Research Questionsmentioning

confidence: 99%

“…The developed machine learning method is named an automatic text classification (MLearn-ATC) model for classifying the different benchmark and real datasets based on the relevant selected features using OTFS. Thiyagarajan and Shanthi [48], proposed a crossover mechanism alongside the Artificial Fish Swarm Algorithm (AFSA) to alleviate the problem of multiplicity, which resulted in a new FS method called the Modified Artificial Fish Swarm Algorithm (MAFSA). Ada-boost, SVM, and the NB classifiers were used to evaluate the feature subsets selected using AFSA and perform the classification.…”

Section: B: Modified/improved Metaheuristic Methodsmentioning

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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Section: Rq2: Does Applying Metaheuristic Algorithms For Tc Lead To B...mentioning

confidence: 81%

Section: B Discussion Of Findings Based On the Research Questionsmentioning

confidence: 99%

Section: B: Modified/improved Metaheuristic Methodsmentioning

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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“…Step 10: End There are two general approaches for symbolizing a document using a list of features, namely the local dictionary technique and the global dictionary methodology [13,18]. The international dictionary will be built using just relevant texts.…”

Section: Algorithm For Creating a Bow Modelmentioning

confidence: 99%

Securing Cloud Data: A Machine Learning based Data Categorization Approach for Cloud Computing

Ahmad

Nawaz

Ali

et al. 2022

Preprint

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Cloud computing (CC) a new systematic model that allows users to store data on remote servers that are accessible via the internet. Due to this approach, the personal and essential data are being stored with easy to access and move. Because of this, it demands is increasing day by day. On this, one can store different data such as financial transactions, paperwork based files and multimedia content. Not only this, but CC also reduce the services dependency on local storage by reducing operational and maintenance costs. Existing systems such as encrypt all data with the same key size, regardless of the data's level of confidentiality due to which the processing cost and time become increased. Moreover, all such techniques classify the data with a low accuracy rate and don’t provide better confidentiality. In this research, a cloud computing approach based on automated data classification has been presented for data sensitivity. The proposed model is based on three level of sensitivity i.e. basic, confidential and highly confidential using Random Forest (RF), Naïve Bayes (NB), k-nearest neighbor (KNN) and support vector machine (SVM) classifiers for training the proposed model with automated feature extraction. The proposed model achieved 92% accuracy that has been showed in simulation results. From this, we conclude that RF, NB, KNN performs better than SVM. The proposed research also provides useful guidelines for cloud service providers (e.g., drop box and Google drive) and researchers.

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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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A modified multi objective heuristic for effective feature selection in text classification

Cited by 12 publications

References 18 publications

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

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

Securing Cloud Data: A Machine Learning based Data Categorization Approach for Cloud Computing

A Chaotic Antlion Optimization Algorithm for Text Feature Selection

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