Feature subset selection wrapper based on mutual information and rough sets

Foithong, Sombut; Pinngern, Ouen; Attachoo, Boonwat

doi:10.1016/j.eswa.2011.07.048

Cited by 80 publications

(47 citation statements)

References 25 publications

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“…The rough set construct can be used to identify a reduced version of the original set of attributes pertaining to a decision system. As a consequence, rough sets found considerable application in feature selection and classification systems (Thangavel and Pethalakshmi 2009;Swiniarski and Skowron 2003;Foithong et al 2012).…”

Section: Granular Computing As a General Data Analysis Frameworkmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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Data granulation emerged as an important paradigm in modeling and computing with uncertainty, exploiting information granules as the main mathematical constructs involved in the context of granular computing. In this paper, we comment on the importance of data granulation in computational intelligence methods. Toward this aim, we discuss also the peculiar aspects related to the analysis of non-geometric patterns, which have recently attracted considerable attention by researchers. As a conclusion, we elaborate over the fundamental, conceptual problems underlying the process of data granulation, which drive the quest for a sound theory of granular computing.

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Section: Granular Computing As a General Data Analysis Frameworkmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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“…The evaluation is independently performed against different features, and the evaluation result called "feature rank" is directly used to define the usefulness of each feature for classification. Entropy and Mutual information are popular ranking methods to evaluate the relevancy of features [Foithong et al, 2012] [Peng et al, 2005] [Javed et al, 2012] [Estevez et al, 2009]. Zhou et al [Zhou et al, 2011] used the Rényi entropy for feature relevance evaluation of overall inputs in their automatic scaling SVM.…”

Section: Related Workmentioning

confidence: 99%

k-fold Subsampling based Sequential Backward Feature Elimination

Park

Kang

Zhou

2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

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Abstract:We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid feature selection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimal feature vector that well represents the shapes of the subjects in the images. In detail, the proposed feature selection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while the standard linear support vector machine (SVM) is used as the classifier for human detection. We apply the proposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCAL VOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approach can improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy. Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach with around 9% improvement in the detection accuracy.

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“…Due to this problem, feature reduction methods are employed in order to alleviate the storage and time requirements of large feature vectors. There are many feature reduction methods, including the linear projection methods such as the Principal Component Analysis (PCA) and the Linear Discriminate Analysis (LDA), and the metric embedding techniques (both linear and non-linear) [4]. However, these methods could not improve the image retrieval performance and efficiently reduce the semantic gap.…”

Section: Introductionmentioning

confidence: 99%

“…These redundant features could influence further analysis in the wrong direction. Consequently, from these significant features, semantic rules are then extracted that can classify the images more accurately and show more relevance images to the user hence improving the retrieval performance [4]. In addition, in this system relevance feedback is used to bridge the semantic gap.…”

Section: Introductionmentioning

confidence: 99%

Utilising Fuzzy Rough Set Based on Mutual Information Decreasing Method for Feature Reduction in an Image Retrieval System

Lotfabadi

Shiratuddin

Wong

2014

Lecture Notes in Electrical Engineering

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Content-Based Image Retrieval (CBIR) system has become a focus of research in the area of image processing and machine vision. General CBIR system automatically index and retrieve images with visual features such as colour, texture and shape. However, current research found that there is a significant gap between visual features and semantic features used by humans to describe images. In order to bridge the semantic gap, some researchers have proposed methods for managing and decreasing image features, and extract useful features from a feature vector. This paper presents an image retrieval system utilising fuzzy rough set based on mutual information decreasing method and the Support Vector Machine (SVM) classifier. The system has training and testing phases. In order to reduce the semantic gap, the propose retrieval system used relevance feedback to improve the retrieval performance. This paper also compared the proposed method with other traditional retrieval systems that use PCA, kernel PCA, Isomap and MVU for their feature reduction method. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the propose method can retrieve images more efficiently than the traditional methods. The use of fuzzy rough set based on mutual information decreasing method, SVM and relevance feedback ensures that the propose image retrieval system produces results which are highly relevant to the content of an image query.

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Feature subset selection wrapper based on mutual information and rough sets

Cited by 80 publications

References 25 publications

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

k-fold Subsampling based Sequential Backward Feature Elimination

Utilising Fuzzy Rough Set Based on Mutual Information Decreasing Method for Feature Reduction in an Image Retrieval System

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