The nature of statistical learning theory
The nature of statistical learning theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis
IEEE Transactions on Knowledge and Data Engineering
Rough Set Based Generalized Fuzzy -Means Algorithm and Quantitative Indices
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy probabilistic approximation spaces and their information measures
IEEE Transactions on Fuzzy Systems
International Journal of Approximate Reasoning
Dynamic rough clustering and its applications
Applied Soft Computing
International Journal of Approximate Reasoning
A novel class dependent feature selection method for cancer biomarker discovery
Computers in Biology and Medicine
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Several information measures such as entropy, mutual information, and f-information have been shown to be successful for selecting a set of relevant and nonredundant genes from a high-dimensional microarray data set. However, for continuous gene expression values, it is very difficult to find the true density functions and to perform the integrations required to compute different information measures. In this regard, the concept of the fuzzy equivalence partition matrix is presented to approximate the true marginal and joint distributions of continuous gene expression values. The fuzzy equivalence partition matrix is based on the theory of fuzzy-rough sets, where each row of the matrix represents a fuzzy equivalence partition that can automatically be derived from the given expression values. The performance of the proposed approach is compared with that of existing approaches using the class separability index and the predictive accuracy of the support vector machine. An important finding, however, is that the proposed approach is shown to be effective for selecting relevant and nonredundant continuous-valued genes from microarray data.