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EWCBR
2006
Springer
13 years 7 months ago
Rough Set Feature Selection Algorithms for Textual Case-Based Classification
Feature selection algorithms can reduce the high dimensionality of textual cases and increase case-based task performance. However, conventional algorithms (e.g., information gain)...
Kalyan Moy Gupta, David W. Aha, Philip Moore
ICCBR
2007
Springer
13 years 9 months ago
Catching the Drift: Using Feature-Free Case-Based Reasoning for Spam Filtering
In this paper, we compare case-based spam filters, focusing on their resilience to concept drift. In particular, we evaluate how to track concept drift using a case-based spam fi...
Sarah Jane Delany, Derek G. Bridge
KBS
2008
98views more  KBS 2008»
13 years 2 months ago
Mixed feature selection based on granulation and approximation
Feature subset selection presents a common challenge for the applications where data with tens or hundreds of features are available. Existing feature selection algorithms are mai...
Qinghua Hu, Jinfu Liu, Daren Yu
ASIAMS
2007
IEEE
13 years 10 months ago
Rough-Fuzzy Granulation, Rough Entropy and Image Segmentation
This talk has two parts explaining the significance of Rough sets in granular computing in terms of rough set rules and in uncertainty handling in terms of lower and upper approxi...
Sankar K. Pal
JCIT
2008
172views more  JCIT 2008»
13 years 3 months ago
Rough Wavelet Hybrid Image Classification Scheme
This paper introduces a new computer-aided classification system for detection of prostate cancer in Transrectal Ultrasound images (TRUS). To increase the efficiency of the comput...
Hala S. Own, Aboul Ella Hassanien