Divergence measures for statistical data processing—An annotated bibliography
M Basseville - Signal Processing, 2013 - Elsevier
Divergence measures for statistical data processing—An annotated bibliography -
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Information granularity in fuzzy binary GrC model
Y Qian, J Liang, ZW Wei-zhi… - IEEE Transactions on …, 2010 - ieeexplore.ieee.org
Zadeh's seminal work in theory of fuzzy-information granulation in human reasoning is
inspired by the ways in which humans granulate information and reason with it. This has led …
inspired by the ways in which humans granulate information and reason with it. This has led …
Simultaneous feature selection and weighting–an evolutionary multi-objective optimization approach
Selection of feature subset is a preprocessing step in computational learning, and it serves
several purposes like reducing the dimensionality of a dataset, decreasing the …
several purposes like reducing the dimensionality of a dataset, decreasing the …
Rough set based maximum relevance-maximum significance criterion and gene selection from microarray data
Among the large amount of genes presented in microarray gene expression data, only a
small fraction of them is effective for performing a certain diagnostic test. In this regard, a …
small fraction of them is effective for performing a certain diagnostic test. In this regard, a …
An improved attribute reduction scheme with covering based rough sets
Attribute reduction is viewed as an important preprocessing step for pattern recognition and
data mining. Most of researches are focused on attribute reduction by using rough sets …
data mining. Most of researches are focused on attribute reduction by using rough sets …
Solid waste bin level detection using gray level co-occurrence matrix feature extraction approach
This paper presents solid waste bin level detection and classification using gray level co-
occurrence matrix (GLCM) feature extraction methods. GLCM parameters, such as …
occurrence matrix (GLCM) feature extraction methods. GLCM parameters, such as …
A relative decision entropy-based feature selection approach
F Jiang, Y Sui, L Zhou - Pattern Recognition, 2015 - Elsevier
Rough set theory has been proven to be an effective tool for feature selection. To avoid the
exponential computation in exhaustive methods, many heuristic feature selection algorithms …
exponential computation in exhaustive methods, many heuristic feature selection algorithms …
Feature Selection for Unbalanced Distribution Hybrid Data Based on -Nearest Neighborhood Rough Set
W Xu, Z Yuan, Z Liu - IEEE Transactions on Artificial …, 2023 - ieeexplore.ieee.org
Neighborhood rough sets are now widely used to process numerical data. Nevertheless,
most of the existing neighborhood rough sets are not able to distinguish class mixture …
most of the existing neighborhood rough sets are not able to distinguish class mixture …
A rough hypercuboid approach for feature selection in approximation spaces
P Maji - IEEE Transactions on Knowledge and Data …, 2012 - ieeexplore.ieee.org
The selection of relevant and significant features is an important problem particularly for data
sets with large number of features. In this regard, a new feature selection algorithm is …
sets with large number of features. In this regard, a new feature selection algorithm is …
An automated solid waste bin level detection system using a gray level aura matrix
An advanced image processing approach integrated with communication technologies and
a camera for waste bin level detection has been presented. The proposed system is …
a camera for waste bin level detection has been presented. The proposed system is …