Ensemble of Feature Selection Methods for Text Classification: An Analytical Study

Guru, D. S.; Suhil, Mahamad; Pavithra, S.; Priya, G. R.

doi:10.1007/978-3-319-76348-4_33

Cited by 14 publications

(12 citation statements)

References 26 publications

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“…We have used Python 3.7.3 for programming and matplotlib library to plot the performance graph. The performance of the proposed work FS-DFTF is shown in Figures 2,3,4,5 and Tables 14,15,16,17,18,19,20,21 on the above mentioned dataset respectively. In all the graphs, the X-axis represents the number of selected features and the Y-axis represents the corresponding classifier performance in terms of accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…For example, if a word appears in all the text documents in the text corpus, that word is not at all useful to predict the class label. In order to reduce the dimension of the feature space as well as to improve the accuracy in text classification problems, feature selection plays a vital role [5,11,12,13,17,9,16]. Let F be the feature set having 'f' number of features, then we can coin the 2 f − 1 (except empty set) number of different subsets of features.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Feature Subset Selection Based on Combining Document Frequency and Term Frequency for Text Classification

Thirumoorthy¹,

Karuppaiah²

2020

cai

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Feature selection plays a vital role to reduce the high dimension of the feature space in the text document classification problem. The dimension reduction of feature space reduces the computation cost and improves the text classification system accuracy. Hence, the identification of a proper subset of the significant features of the text corpus is needed to classify the data in less computational time with higher accuracy. In this proposed research, a novel feature selection method which combines the document frequency and the term frequency (FS-DFTF) is used to measure the significance of a term. The optimal feature subset which is selected by our proposed work is evaluated using Naive Bayes and Support Vector Machine classifier with various popular benchmark text corpus datasets. The experimental outcome confirms that the proposed method has a better classification accuracy when compared with other feature selection techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Feature Subset Selection Based on Combining Document Frequency and Term Frequency for Text Classification

Thirumoorthy¹,

Karuppaiah²

2020

cai

View full text Add to dashboard Cite

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“…Comparison of homogeneous and heterogeneous Ensemble Feature Selection techniques is done in studies [11], [42]. In a study by B. Seijo-Pardo et al (2017) [11] Ensemble Learning approaches, i.e.…”

Section: B Ensemble Feature Selectionmentioning

confidence: 99%

“…Though some studies have suggested a number of aggregators [11], [42], [44], none of the studies have considered the aggregators proposed in this work. Our study proposes using aggregators such as intersection, union and subtraction.…”

Section: E Ensemble Techniques Aggregatorsmentioning

confidence: 99%

A Novel Four-Way Approach Designed With Ensemble Feature Selection for Code Smell Detection

Kaur

2021

IEEE Access

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A. PURPOSECode smells are residuals of technical debt induced by the developers. They hinder evolution, adaptability and maintenance of the software. Meanwhile, they are very beneficial in indicating the loopholes of problems and bugs in the software. Machine learning has been extensively used to predict Code Smells in research. The current study aims to optimise the prediction using Ensemble Learning and Feature Selection techniques on three open-source Java data sets. B. DESIGN AND RESULTSThe work Compares four varied approaches to detect code smells using four performance measures Accuracy(P1), G-mean1 (P2), G-mean2 (P3), and F-measure (P4). The study found out that values of the performance measures did not degrade it instead of either remained same or increased with feature selection and Ensemble Learning. Random Forest turns out to be the best classifier while Correlation-based Feature selection(BFS) is best amongst Feature Selection techniques. Ensemble Learning aggregators, i.e. ET5C2 (BFS intersection Relief with classifier Random Forest), ET6C2 (BFS union Relief with classifier Random Forest), and ET5C1 (BFS intersection Relief with Bagging) and Majority Voting give best results from all the aggregation combinations studied. C. CONCLUSIONSThough the results are good, but using Ensemble learning techniques needs a lot of validation for a variety of data sets before it can be standardised. The Ensemble Learning techniques also pose a challenge concerning diversity and reliability and hence needs exhaustive studies.

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“…They validate their methods with small scale and also high dimensional datasets. There are some other studies in the literature for ensemble feature selection methods [33] [34].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets

et al. 2020

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It is of great importance to extract and validate an optimal subset of non-dominated features for effective multi-label classification. However, deciding on the best subset of features is an NP-Hard problem and plays a key role in improving the prediction accuracy and the processing time of video datasets. In this study, we propose autoencoders for dimensionality reduction of video data sets and ensemble the features extracted by the multi-objective evolutionary Non-dominated Sorting Genetic Algorithm and the autoencoder. We explore the performance of well-known multi-label classification algorithms for video datasets in terms of prediction accuracy and the number of features used. More specifically, we evaluate Non-dominated Sorting Genetic Algorithm-II, autoencoders, ensemble learning algorithms, Principal Component Analysis, Information Gain, and Correlation Based Feature Selection. Some of these algorithms use feature selection techniques to improve the accuracy of the classification. Experiments are carried out with local feature descriptors extracted from two multi-label datasets, the MIR-Flickr dataset which consists of images and the Wireless Multimedia Sensor dataset that we have generated from our video recordings. Significant improvements in the accuracy performance of the algorithms are observed while the number of features are being reduced.

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Ensemble of Feature Selection Methods for Text Classification: An Analytical Study

Cited by 14 publications

References 26 publications

Optimal Feature Subset Selection Based on Combining Document Frequency and Term Frequency for Text Classification

Optimal Feature Subset Selection Based on Combining Document Frequency and Term Frequency for Text Classification

A Novel Four-Way Approach Designed With Ensemble Feature Selection for Code Smell Detection

Analysis of Multiobjective Algorithms for the Classification of Multi-Label Video Datasets

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