A Comparison of Feature Selection Methods to Optimize Predictive Models Based on Decision Forest Algorithms for Academic Data Analysis

Fernández-García, Antonio Jesús; Iribarne, Luís; Corral, Antonio; Criado, Javier

doi:10.1007/978-3-319-77703-0_35

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…The other applications of decision forest were prediction of different types of liver diseases including alcoholic, liver damage and liver cirrhosis [66]. Other than biomedical classification, Decision forest method was applied for academic data analysis [67] as well as classification and forecasting of chronic kidney disease [68]. Decision Jungles were used for feature selection for images with some modification to achieve efficient results with modest training time [69].…”

Section: Resultsmentioning

confidence: 99%

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Alam¹,

Milhan²,

Atif³

et al. 2019

ijacsa

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Cervical cancer remains an important reason of deaths worldwide because effective access to cervical screening methods is a big challenge. Data mining techniques including decision tree algorithms are used in biomedical research for predictive analysis. The imbalanced dataset was obtained from the dataset archive belongs to the University of California, Irvine. Synthetic Minority Oversampling Technique (SMOTE) has been used to balance the dataset in which the number of instances has increased. The dataset consists of patient age, number of pregnancies, contraceptives usage, smoking patterns and chronological records of sexually transmitted diseases (STDs). Microsoft azure machine learning tool was used for simulation of results. This paper mainly focuses on cervical cancer prediction through different screening methods using data mining techniques like Boosted decision tree, decision forest and decision jungle algorithms as well performance evaluation has done on the basis of AUROC (Area under Receiver operating characteristic) curve, accuracy, specificity and sensitivity. 10-fold cross-validation method was utilized to authenticate the results and Boosted decision tree has given the best results. Boosted decision tree provided very high prediction with 0.978 on AUROC curve while Hinslemann screening method has used. The results obtained by other classifiers were significantly worse than boosted decision tree.

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Section: Resultsmentioning

confidence: 99%

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Alam¹,

Milhan²,

Atif³

et al. 2019

ijacsa

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show abstract

“…For that purpose, after calculating the correlation coefficients' values, we performed significance analysis. A similar approach was followed in [68], where only the Pearson, Spearman, and Kendall correlation, mutual information (information gain), and χ 2 ranking technique were used. All of these methods can be used for ranking the features for the purpose of feature selection (choosing features with the best rank values for a particular method); however, their nature and purpose are different.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Entropy based techniques and the χ 2 ranking technique, on the other hand, can be used when dealing with all types of features, and they are based on the statistical properties of the variables. These methods are commonly used in the feature selection domain [68]. Nevertheless, these methods are not perfect either, e.g., the χ 2 ranking technique does not perform well while dealing with infrequent terms in data [72], and information gain favors features with many uniformly distributed values [73].…”

Section: Feature Selectionmentioning

confidence: 99%

“…Nevertheless, these methods are not perfect either, e.g., the χ 2 ranking technique does not perform well while dealing with infrequent terms in data [72], and information gain favors features with many uniformly distributed values [73]. For that reason, it is a good practice to test different types of feature selection and to perform the additional evaluation, e.g., machine learning based evaluation, which was also done in [68] and in our work (described in the next subsection).…”

Section: Feature Selectionmentioning

confidence: 99%

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Efficient Feature Selection for Static Analysis Vulnerability Prediction

Filus

Boryszko

Domańska

et al. 2021

Sensors

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Common software vulnerabilities can result in severe security breaches, financial losses, and reputation deterioration and require research effort to improve software security. The acceleration of the software production cycle, limited testing resources, and the lack of security expertise among programmers require the identification of efficient software vulnerability predictors to highlight the system components on which testing should be focused. Although static code analyzers are often used to improve software quality together with machine learning and data mining for software vulnerability prediction, the work regarding the selection and evaluation of different types of relevant vulnerability features is still limited. Thus, in this paper, we examine features generated by SonarQube and CCCC tools, to identify those that can be used for software vulnerability prediction. We investigate the suitability of thirty-three different features to train thirteen distinct machine learning algorithms to design vulnerability predictors and identify the most relevant features that should be used for training. Our evaluation is based on a comprehensive feature selection process based on the correlation analysis of the features, together with four well-known feature selection techniques. Our experiments, using a large publicly available dataset, facilitate the evaluation and result in the identification of small, but efficient sets of features for software vulnerability prediction.

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“…Ghaemi et al found FOA can improve the classification accuracy comparing with other feature selection methods [Ghaemi and Feizi-Derakhshi (2016)]. Fernández-García et al compared the FOA with other feature selection method on Academic Data to foresee if students will finish their degree after finishing their first year in college [Fernández-García, Iribarne, Corral et al (2018)]. Although the FOA get the better result in above experiments, the search efficiency is still low and the algorithm complexity is relative higher.…”

Section: Foamentioning

confidence: 99%

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Ma¹,

Zhou²,

Jia³

et al. 2019

Computer Modeling in Engineering &Amp; Sciences

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Feature selection has been widely used in data mining and machine learning. Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly. In this article, a feature selection algorithm with local search strategy based on the forest optimization algorithm, namely FSLSFOA, is proposed. The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest. Next, the fitness function is improved, which not only considers the classification accuracy, but also considers the size of the feature subset. To avoid falling into local optimum, a novel global seeding method is attempted, which selects trees on the bottom of candidate set and gives the algorithm more diversities. Finally, FSLSFOA is compared with four feature selection methods to verify its effectiveness. Most of the results are superior to these comparative methods.

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A Comparison of Feature Selection Methods to Optimize Predictive Models Based on Decision Forest Algorithms for Academic Data Analysis

Cited by 6 publications

References 6 publications

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Cervical Cancer Prediction through Different Screening Methods using Data Mining

Efficient Feature Selection for Static Analysis Vulnerability Prediction

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

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