Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory

Zeng, Kai; She, Kun; Niu, Xinjian

doi:10.1155/2014/479289

Cited by 12 publications

(10 citation statements)

References 16 publications

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“…For the second stage, the generated features are streamed sequentially into the processing window. After the number of features in the cache window reaches the window size again, the value of the feature repulsion loss function is calculated for all candidate features in the cache window according to (12). Hence, the candidate features with the largest feature repulsion loss (FRL) are saved to the selected feature subset.…”

Section: Streaming Feature Selectionmentioning

confidence: 99%

“…In this paper, we consider only filtering methods which seek to design effective metrics for evaluating the importance of candidate features. Such metrics can be based on information entropy [11][12][13][14], the classification boundary [15], and rough sets [16,17]. In particular, Lee et al [18] proposed a multivariate mutual information criterion for multi-label feature selection.…”

Section: Introductionmentioning

confidence: 99%

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Streaming Feature Selection for Multi-Label Data with Dynamic Sliding Windows and Feature Repulsion Loss

Cheng

2019

Entropy

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In recent years, there has been a growing interest in the problem of multi-label streaming feature selection with no prior knowledge of the feature space. However, the algorithms proposed to handle this problem seldom consider the group structure of streaming features. Another shortcoming arises from the fact that few studies have addressed atomic feature models, and particularly, few have measured the attraction and repulsion between features. To remedy these shortcomings, we develop the streaming feature selection algorithm with dynamic sliding windows and feature repulsion loss (SF-DSW-FRL). This algorithm is essentially carried out in three consecutive steps. Firstly, within dynamic sliding windows, candidate streaming features that are strongly related to the labels in different feature groups are selected and stored in a fixed sliding window. Then, the interaction between features is measured by a loss function inspired by the mutual repulsion and attraction between atoms in physics. Specifically, one feature attraction term and two feature repulsion terms are constructed and combined to create the feature repulsion loss function. Finally, for the fixed sliding window, the best feature subset is selected according to this loss function. The effectiveness of the proposed algorithm is demonstrated through experiments on several multi-label datasets, statistical hypothesis testing, and stability analysis.

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Section: Streaming Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Streaming Feature Selection for Multi-Label Data with Dynamic Sliding Windows and Feature Repulsion Loss

Cheng

2019

Entropy

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“…We use a forward greedy search algorithm, which is usually more efficient than a standard brute-force exhaustive search [4]. That is, one starts with an empty set of attributes and adds features to the subset of selected attributes one by one.…”

Section: Feature Selection Based On Knrsmentioning

confidence: 99%

“…This theory has been successfully applied to many fields, such as data mining, decision-making, pattern recognition, machine learning, and intelligent control [1][2][3][4]. Kernel rough sets [5] and neighborhood rough sets [6] are two important models in rough set theory.…”

Section: Introductionmentioning

confidence: 99%

Kernel Neighborhood Rough Sets Model and Its Application

Zeng

Jing

2018

Complexity

Self Cite

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Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation. The kernel rough sets model, which has fuzziness, is susceptible to noise in the decision system. The neighborhood rough sets model can handle noisy data well but cannot describe the fuzziness of the samples. In this study, we define a novel model called kernel neighborhood rough sets, which integrates the advantages of the neighborhood and kernel models. Moreover, the model is used in the problem of feature selection. The proposed method is tested on the UCI datasets. The results show that our model outperforms classic models.

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“…Paper [ 3 ] used the Banzhaf power index in game theory for evaluating the weight of each feature, while paper [ 21 ] used the Shapley value index in game theory for evaluating the weight of each feature. Reference [ 22 ] presented a novel feature selection approach called Neighborhood Entropy-Based Cooperative Game Theory (NECGT) which was based on information theoretic approaches. The results of the evaluation of some UCI data sets showed that the approach yields better performance compared to classical methods in terms of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

Mortazavi

Moattar

2016

Advances in Bioinformatics

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High dimensionality of microarray data sets may lead to low efficiency and overfitting. In this paper, a multiphase cooperative game theoretic feature selection approach is proposed for microarray data classification. In the first phase, due to high dimension of microarray data sets, the features are reduced using one of the two filter-based feature selection methods, namely, mutual information and Fisher ratio. In the second phase, Shapley index is used to evaluate the power of each feature. The main innovation of the proposed approach is to employ Qualitative Mutual Information (QMI) for this purpose. The idea of Qualitative Mutual Information causes the selected features to have more stability and this stability helps to deal with the problem of data imbalance and scarcity. In the third phase, a forward selection scheme is applied which uses a scoring function to weight each feature. The performance of the proposed method is compared with other popular feature selection algorithms such as Fisher ratio, minimum redundancy maximum relevance, and previous works on cooperative game based feature selection. The average classification accuracy on eleven microarray data sets shows that the proposed method improves both average accuracy and average stability compared to other approaches.

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Feature Selection with Neighborhood Entropy-Based Cooperative Game Theory

Cited by 12 publications

References 16 publications

Streaming Feature Selection for Multi-Label Data with Dynamic Sliding Windows and Feature Repulsion Loss

Streaming Feature Selection for Multi-Label Data with Dynamic Sliding Windows and Feature Repulsion Loss

Kernel Neighborhood Rough Sets Model and Its Application

Robust Feature Selection from Microarray Data Based on Cooperative Game Theory and Qualitative Mutual Information

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