Majority vote feature selection algorithm in software fault prediction

Borandağ, Emin; Özçift, Akın; Kılınç, Deniz; Yücalar, Fatih

doi:10.2298/csis180312039b

Cited by 20 publications

(8 citation statements)

References 27 publications

(30 reference statements)

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“…The methods namely Information gain, ReliefF, analysis of variance (ANOVA), and Chi‐square methods were chosen as filter‐based feature selection methods based on their popularities and advantages (Borandag, Ozcift, Kilinc, & Yucalar, 2019). Wrapper and embedded based feature selection methods are computationally very expensive (Borandag et al, 2019). However, filter‐based feature selection methods are both faster and less expensive compared to the wrapper and embedded methods.…”

Section: Methodsmentioning

confidence: 99%

New hybrid data mining model for prediction of Salmonella presence in agricultural waters based on ensemble feature selection and machine learning algorithms

Buyrukoğlu

2021

Journal of Food Safety

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This paper aims to create a new hybrid ensemble data mining model to predict the Salmonella presence in agricultural surface waters based on the combination of heterogeneous ensemble approach for feature selection, clustering, regression, and classification algorithms. The data set for this study was collected from six agricultural ponds in Central Florida consisting of 23 features with 540 instances (26 Salmonella positive and 514 Salmonella negative). The model consisted of three stages. Initially, a heterogeneous ensemble feature selection (HEFS) approach was applied to select top features. Then, the k‐means clustering algorithm was implemented to remove misclassified cases from the data set. Finally, classification and regression algorithms, including support vector machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN), Random Forest (RF) with soft voting approach were applied to the preprocessed data set to predict the Salmonella presence in agricultural surface waters with the amount of test set (20%). These algorithms were combined in 10 different ensemble models through the soft voting approach. The performance of these hybrid ensemble models was also evaluated. The ensemble ANN + RF model achieved the highest performance and outperformed all other single and ensemble models based on Area under the ROC Curve (AUC) (0.98) and prediction accuracy (94.9%). The findings emphasize the validity of our hybrid ensemble model which encourages researchers to predict Salmonella presence in agricultural surface waters.

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Section: Methodsmentioning

confidence: 99%

New hybrid data mining model for prediction of Salmonella presence in agricultural waters based on ensemble feature selection and machine learning algorithms

Buyrukoğlu

2021

Journal of Food Safety

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“…Feature selection processes have been applied in various fields. The authors in [4] aimed of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics and the thorough experiments showed the ability of the proposed method to find out the most significant software metrics that enhance defect prediction performance. On the other hand, the authors in [30] analyzed the correlations among different commodities sales to identify interesting patterns to increase cross-marketing quality.…”

Section: Proposed Methodology 41 Motivationmentioning

confidence: 99%

“…For a comprehensive description of the technical indicators and their use see [34], [10] and Table 2 in [49]. Note that these technical indicators are computed using an open source library ta-lib 4 . At this stage, for each timestamp t the data vectorx t (enhanced vector x t ) contains features extracted from the LOB shape (e.g.…”

Section: The Market Data Summary and Data Processing Reconstructormentioning

confidence: 99%

A multicriteria optimization approach for the stock market feature selection

Radojičić

Kredatus

2021

ComSIS

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This paper studies the informativeness of features extracted from a limit order book data, to classify market data vector into the label (buy/idle) by using the Long short-term memory (LSTM) network. New technical indicators based on the support/resistance zones are introduced to enrich the set of features. We evaluate whether the performance of the LSTM network model is improved when we select features with respect to the newly proposed methods. Moreover, we employ multicriteria optimization to perform adequate feature selection among the proposed approaches, with respect to precision, recall, and F? score. Seven variations of approaches to select features are proposed and the best is selected by incorporation of multicriteria optimization.

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“…Ensemble Learning has been broadly utilized in the classification [32]- [35] however, is not restricted to it. The use of Ensemble Learning is now being studied in Feature Selection area where it very well may be utilized to diminish high dimensional data sets into low dimensional.…”

Section: B Ensemble Feature Selectionmentioning

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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Majority vote feature selection algorithm in software fault prediction

Cited by 20 publications

References 27 publications

New hybrid data mining model for prediction of Salmonella presence in agricultural waters based on ensemble feature selection and machine learning algorithms

New hybrid data mining model for prediction of Salmonella presence in agricultural waters based on ensemble feature selection and machine learning algorithms

A multicriteria optimization approach for the stock market feature selection

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

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