A Classification Method Based on Feature Selection for Imbalanced Data

Liu, Yi; Wang, Yanzhen; Ren, Xiaobing; Zhou, Hao; Diao, Xingchun

doi:10.1109/access.2019.2923846

Cited by 54 publications

(25 citation statements)

References 45 publications

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“…For a fair evaluation, all of the methods examined in this section were selected from among wrapper-based methods. These wrapper-based methods include PSO-based [61], ACO-based [62], and ABC-based [63] 4…”

Section: Experiments and Discussionmentioning

confidence: 99%

A Novel Community Detection Based Genetic Algorithm for Feature Selection

Rostami

Berahmand

Forouzandeh

2020

Preprint

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The selection of features is an essential data preprocessing stage in data mining. The core principle of feature selection seems to be to pick a subset of possible features by excluding features with almost no predictive information as well as highly associated redundant features. In the past several years, a variety of meta-heuristic methods were introduced to eliminate redundant and irrelevant features as much as possible from high-dimensional datasets. Among the main disadvantages of present meta-heuristic based approaches is that they are often neglecting the correlation between a set of selected features. In this article, for the purpose of feature selection, the authors propose a genetic algorithm based on community detection, which functions in three steps. The feature similarities are calculated in the first step. The features are classified by community detection algorithms into clusters throughout the second step. In the third step, features are picked by a genetic algorithm with a new community-based repair operation. Nine benchmark classification problems were analyzed in terms of the performance of the presented approach. Also, the authors have compared the efficiency of the proposed approach with the findings from four available algorithms for feature selection. The findings indicate that the new approach continuously yields improved classification accuracy.

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Section: Experiments and Discussionmentioning

confidence: 99%

A Novel Community Detection Based Genetic Algorithm for Feature Selection

Rostami

Berahmand

Forouzandeh

2020

Preprint

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“…The experiments have been performed in Waikato Environment for Knowledge Analysis (WEKA) [19]. WEKA, acknowledged as a landmark system in machine learning and DM, has become a widely used tool for DM research [20].…”

Section: Methodsmentioning

confidence: 99%

Conversion of adverse data corpus to shrewd output using sampling metrics

Ashraf

Saleem

Ahmed

et al. 2020

Vis. Comput. Ind. Biomed. Art

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An imbalanced dataset is commonly found in at least one class, which are typically exceeded by the other ones. A machine learning algorithm (classifier) trained with an imbalanced dataset predicts the majority class (frequently occurring) more than the other minority classes (rarely occurring). Training with an imbalanced dataset poses challenges for classifiers; however, applying suitable techniques for reducing class imbalance issues can enhance classifiers' performance. In this study, we consider an imbalanced dataset from an educational context. Initially, we examine all shortcomings regarding the classification of an imbalanced dataset. Then, we apply data-level algorithms for class balancing and compare the performance of classifiers. The performance of the classifiers is measured using the underlying information in their confusion matrices, such as accuracy, precision, recall, and F measure. The results show that classification with an imbalanced dataset may produce high accuracy but low precision and recall for the minority class. The analysis confirms that undersampling and oversampling are effective for balancing datasets, but the latter dominates.

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“…This feature selection technique works by measuring the uncertainty of a random variable x to another variable y as given by (6) and 7. P(xi) is the prior probability for all values of x and P(xi |yi) is the posterior probability of x given y [54], [55]. It is claimed to be effective in feature selection for large scale data sets.…”

Section: Symmetrical Uncertainty (Su)mentioning

confidence: 99%