Rough sets and fuzzy sets—some remarks on interrelations
Fuzzy Sets and Systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: concepts and techniques
Data mining: concepts and techniques
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Feature Selection for Clustering
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Rough sets perspective on data and knowledge
Handbook of data mining and knowledge discovery
ICCI '04 Proceedings of the Third IEEE International Conference on Cognitive Informatics
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Feature Selection with a Linear Dependence Measure
IEEE Transactions on Computers
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Rough Computing: Theories, Technologies and Applications
Rough Computing: Theories, Technologies and Applications
Measures for Unsupervised Fuzzy-Rough Feature Selection
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
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Feature Selection (FS) is a process which attempts to select features which are more informative. In this paper, a novel unsupervised FS in mammogram images, using rough set-based relative dependency measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, pre-processing of image, segmentation and features extraction from the segmented mammogram image. The proposed unsupervised FS method is used to select features from data sets; the method is compared with existing rough set based supervised FS methods, and the classification performance of both methods are recorded and demonstrate the efficiency of this method.