Unsupervised neuro-fuzzy feature selection

Basak, Jayanta Kumar; De, Rajonya; Pal, Sankar K.

doi:10.1109/ijcnn.1998.682229

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…For this system, the first filter is to remove redundant feature from original feature set, which can be completed using any clustering algorithm, such as C-means [9]. Here, we will eliminate the redundant features using a specific algorithm newly developed in [23] (we call it as Mitra's) for its high efficiency and low time cost, which is to find the subsets of feature that are highly correlated based on the k nearest neighbors principle.…”

Section: The Proposed Systemmentioning

confidence: 99%

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Unsupervised Feature Selection

Pal¹,

Mitra²

2004

Chapman &Amp; Hall/CRC Computer Science &Amp; Data Analysis

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Abstract. The problem of feature selection has long been an active research topic within statistics and pattern recognition. So far, most methods of feature selection focus on supervised data where class information is available. For unsupervised data, the related methods of feature selection are few. The presented article demonstrates a way of unsupervised feature selection, which is a two-level filter model removing the redundant and irrelevant features, respectively. The redundant features are eliminated using any clustering algorithm, and a new method is proposed to remove the irrelevant features: first rank the features according to their relevance to cluster and then a subset of relevant features is selected using the Fuzzy Feature Evaluation Index (FFEI) with some changes and extensions. The experimental results have shown the effectiveness of the proposed method for high-dimensional data. Our major contributions are: (1) to present a new hierarchical filter method for unsupervised feature selection; (2) to propose a new algorithm for removing the irrelevant features; (3) to extend the FFEI, and present a method for calculating the approximate weight of feature in FFEI, which improves the efficiency and robustness of the method.

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Section: The Proposed Systemmentioning

confidence: 99%

“…The adopted ranking index belongs to exponential entropy. The evaluation criterion is Fuzzy Feature Evaluation Index (FFEI), which is defined in [8,9,25].…”

Section: The Irrelevancy Filtermentioning

confidence: 99%

Unsupervised Feature Selection

Pal¹,

Mitra²

2004

Chapman &Amp; Hall/CRC Computer Science &Amp; Data Analysis

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Feature selection for pattern classification problems

Zhang

Sun

Guo

The Fourth International Conference onComputer and Information Technology, 2004. CIT '04.

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In pattern recognition feature selection is an important problem which is to choose the smallest subset of features that ideally is necessary and sufficient to describe the target concept. In this paper, a feature selection algorithm based on DB index rules is proposed involving classification capabilities of feature vectors and correlation analysis between two features. The strategy can be used for supervised or unsupervised classification problems and it is evaluated by using three synthetic data sets and a real-word data set.

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Unsupervised neuro-fuzzy feature selection

Cited by 2 publications

References 8 publications

Unsupervised Feature Selection

Unsupervised Feature Selection

Feature selection for pattern classification problems

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