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TKDE
2008
115views more  TKDE 2008»
13 years 4 months ago
A Niching Memetic Algorithm for Simultaneous Clustering and Feature Selection
Clustering is inherently a difficult task and is made even more difficult when the selection of relevant features is also an issue. In this paper, we propose an approach for simult...
Weiguo Sheng, Xiaohui Liu, Michael C. Fairhurst
BMCBI
2004
181views more  BMCBI 2004»
13 years 4 months ago
Iterative class discovery and feature selection using Minimal Spanning Trees
Background: Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples ar...
Sudhir Varma, Richard Simon
COR
2010
121views more  COR 2010»
13 years 4 months ago
A multi-objective approach for robust airline scheduling
We present a memetic approach for multi-objective improvement of robustness influencing features (called robustness objectives) in airline schedules. Improvement of the objectives...
Edmund K. Burke, Patrick De Causmaecker, Geert De ...
ICML
1994
IEEE
13 years 8 months ago
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the te...
David B. Skalak
ICIP
2003
IEEE
14 years 6 months ago
Feature selection for unsupervised discovery of statistical temporal structures in video
We present algorithms for automatic feature selection for unsupervised structure discovery from video sequences. Feature selection in this scenario is hard because of the absence ...
Lexing Xie, Shih-Fu Chang, Ajay Divakaran, Huifang...