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ICML
1994
IEEE
13 years 8 months ago
Greedy Attribute Selection
Many real-world domains bless us with a wealth of attributes to use for learning. This blessing is often a curse: most inductive methods generalize worse given too many attributes...
Rich Caruana, Dayne Freitag
ICDE
2008
IEEE
110views Database» more  ICDE 2008»
14 years 6 months ago
Standing Out in a Crowd: Selecting Attributes for Maximum Visibility
In recent years, there has been significant interest in development of ranking functions and efficient top-k retrieval algorithms to help users in ad-hoc search and retrieval in da...
Muhammed Miah, Gautam Das, Vagelis Hristidis, Heik...
WEA
2004
Springer
129views Algorithms» more  WEA 2004»
13 years 10 months ago
Experimental Comparison of Greedy Randomized Adaptive Search Procedures for the Maximum Diversity Problem
Abstract. The maximum diversity problem (MDP) consists of identifying optimally diverse subsets of elements from some larger collection. The selection of elements is based on the d...
Geiza C. Silva, Luiz Satoru Ochi, Simone L. Martin...
KBS
2008
98views more  KBS 2008»
13 years 3 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
ISCI
2008
88views more  ISCI 2008»
13 years 4 months ago
Greedy regression ensemble selection: Theory and an application to water quality prediction
This paper studies the greedy ensemble selection family of algorithms for ensembles of regression models. These algorithms search for the globally best subset of regresmaking loca...
Ioannis Partalas, Grigorios Tsoumakas, Evaggelos V...