Granular-conditional-entropy-based attribute reduction for partially labeled data with proxy labels

Gao, Chengde; Zhou, Jie; Miao, Duoqian; Yue, Xiaodong; Wan, Jun

doi:10.1016/j.ins.2021.08.067

Cited by 30 publications

(3 citation statements)

References 73 publications

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“…For other types of data, it is necessary to remove redundant and noisy attributes. In addition, it can be seen from Figure 7 that compared with other different attribute reduction algorithms (J CG [41], J CH [42], J MG , J MH [43], J JG , J JH , z, x + y + z [44], x + z [19], z-y [45]), the MCIR algorithm (red dotted line) designed in this paper achieves better accuracy with fewer attributes. Since the MCIR model removes as many redundant or noisy attributes from the data as possible and achieves data optimization through data dimensionality reduction, making the reduced data better for anomaly detection tasks, the model can maintain or even improve the accuracy of performance detection while reducing the time complexity.…”

Section: Attribute Reduction Under Mcir Algorithmmentioning

confidence: 94%

TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block

Chen

Liu

et al. 2023

Entropy

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Telecom fraud detection is of great significance in online social networks. Yet the massive, redundant, incomplete, and uncertain network information makes it a challenging task to handle. Hence, this paper mainly uses the correlation of attributes by entropy function to optimize the data quality and then solves the problem of telecommunication fraud detection with incomplete information. First, to filter out redundancy and noise, we propose an attribute reduction algorithm based on max-correlation and max-independence rate (MCIR) to improve data quality. Then, we design a rough-gain anomaly detection algorithm (MCIR-RGAD) using the idea of maximal consistent blocks to deal with missing incomplete data. Finally, the experimental results on authentic telecommunication fraud data and UCI data show that the MCIR-RGAD algorithm provides an effective solution for reducing the computation time, improving the data quality, and processing incomplete data.

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Section: Attribute Reduction Under Mcir Algorithmmentioning

confidence: 94%

TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block

Chen

Liu

et al. 2023

Entropy

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“…The conditional entropy is another important measure corresponding to neighborhood rough set, which can characterize the discriminating performance of ∀A ⊆ AT with respect to d. Thus far, various forms of conditional entropy [50][51][52][53] have been proposed in respect to different requirements. A special form which is widely used is shown below.…”

Section: Conditional Entropymentioning

confidence: 99%

Parallel Selector for Feature Reduction

et al. 2023

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In the field of rough set, feature reduction is a hot topic. Up to now, to better guide the explorations of this topic, various devices regarding feature reduction have been developed. Nevertheless, some challenges regarding these devices should not be ignored: (1) the viewpoint provided by a fixed measure is underabundant; (2) the final reduct based on single constraint is sometimes powerless to data perturbation; (3) the efficiency in deriving the final reduct is inferior. In this study, to improve the effectiveness and efficiency of feature reduction algorithms, a novel framework named parallel selector for feature reduction is reported. Firstly, the granularity of raw features is quantitatively characterized. Secondly, based on these granularity values, the raw features are sorted. Thirdly, the reordered features are evaluated again. Finally, following these two evaluations, the reordered features are divided into groups, and the features satisfying given constraints are parallel selected. Our framework can not only guide a relatively stable feature sequencing if data perturbation occurs but can also reduce time consumption for feature reduction. The experimental results over 25 UCI data sets with four different ratios of noisy labels demonstrated the superiority of our framework through a comparison with eight state-of-the-art algorithms.

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“…There are two mainstream learning perspectives, i.e., supervised learning and unsupervised learning. Then, we pick the approximation quality [34] and conditional entropy [19,[35][36][37][38][39] as two custom measures to better comprehend and investigate the essence of attribute reduction in terms of the neighborhood rough set.…”

Section: Attribute Reductionmentioning

confidence: 99%

Forward Greedy Searching to κ-Reduct Based on Granular Ball

et al. 2023

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As a key part of data preprocessing, namely attribute reduction, is effectively applied in the rough set field. The purpose of attribute reduction is to prevent too many attributes from affecting classifier operations and reduce the dimensionality of data space. Presently, in order to further improve the simplification performance of attribute reduction, numerous researchers have proposed a variety of methods. However, given the current findings, the challenges are: to reasonably compress the search space of candidate attributes; to fulfill multi-perspective evaluation; and to actualize attribute reduction based on guidance. In view of this, forward greedy searching to κ-reduct based on granular ball is proposed, which has the following advantages: (1) forming symmetrical granular balls to actualize the grouping of the universe; (2) continuously merging small universes to provide guidance for subsequent calculations; and (3) combining supervised and unsupervised perspectives to enrich the viewpoint of attribute evaluation and better improve the capability of attribute reduction. Finally, based on three classifiers, 16 UCI datasets are used to compare our proposed method with six advanced algorithms about attribute reduction and an algorithm without applying any attribute reduction algorithms. The experimental results indicate that our method can not only ensure the result of reduction has considerable performance in the classification test, but also improve the stability of attribute reduction to a certain degree.

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Granular-conditional-entropy-based attribute reduction for partially labeled data with proxy labels

Cited by 30 publications

References 73 publications

TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block

TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block

Parallel Selector for Feature Reduction

Forward Greedy Searching to κ-Reduct Based on Granular Ball

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