Journal ArticleDOI
Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules
Pradipta Maji,Sushmita Paul +1 more
- Vol. 40, Iss: 6, pp 639-648
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TLDR
A new feature selection algorithm is presented, based on rough set theory, to select a set of effective molecular descriptors from a given QSAR dataset by maximizing both relevance and significance of the descriptors.Abstract:
Quantitative structure activity relationship (QSAR) is one of the important disciplines of computer-aided drug design that deals with the predictive modeling of properties of a molecule. In general, each QSAR dataset is small in size with large number of features or descriptors. Among the large amount of descriptors presented in the QSAR dataset, only a small fraction of them is effective for performing the predictive modeling task. In this paper, a new feature selection algorithm is presented, based on rough set theory, to select a set of effective molecular descriptors from a given QSAR dataset. The proposed algorithm selects the set of molecular descriptors by maximizing both relevance and significance of the descriptors. An important finding is that the proposed feature selection algorithm is shown to be effective in selecting relevant and significant molecular descriptors from the QSAR dataset for predictive modeling. The performance of the proposed algorithm is studied using R2 statistic of support vector regression method. The effectiveness of the proposed algorithm, along with a comparison with existing algorithms, is demonstrated on three QSAR datasets.read more
Citations
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Journal ArticleDOI
Hyperspectral Band Selection Based on Rough Set
TL;DR: Three state-of-the-art methods used in the remote sensing literature are analyzed for comparison and the results point to the superiority of the proposed rough-set-based supervised technique, especially when a small number of bands are to be selected.
Journal ArticleDOI
An improved attribute reduction scheme with covering based rough sets
TL;DR: A new method for constructing simpler discernibility matrix with covering based rough sets is provided, and some characterizations of attribute reduction provided by Tsang et al. are improved.
Journal ArticleDOI
On fuzzy-rough attribute selection: Criteria of Max-Dependency, Max-Relevance, Min-Redundancy, and Max-Significance
Pradipta Maji,Partha Garai +1 more
TL;DR: The effectiveness of the fuzzy-rough set based attribute selection method, along with a comparison with existing feature evaluation indices and different rough set models, is demonstrated on a set of benchmark and microarray gene expression data sets.
Journal ArticleDOI
A New Feature Selection Method for One-Class Classification Problems
TL;DR: Two support vector data description (SVDD)-based feature selection methods are proposed: SVDD-radius-recursive feature elimination (RFE) and SVDd dual-objective RFE, which show the improved performance compared with existing support vector machine RFE methods even for the classification problems when available observations for the anomaly are few.
Journal ArticleDOI
Attribute reductions in object-oriented concept lattices
TL;DR: This paper mainly deals with attribute reductions of an object-oriented concept lattice constructed on the basis of rough set, and an approach to object- oriented reductions ofAn approach toobject-oriented reductions of a object- Oriented Concept lattice is proposed, and the attribute characteristics are also analyzed.
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Journal ArticleDOI
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Ron Kohavi,George H. John +1 more
TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
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