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ICML
1998
IEEE
13 years 8 months ago
The Problem with Noise and Small Disjuncts
Many systems that learn from examples express the learned concept as a disjunction. Those disjuncts that cover only a few examples are referred to as small disjuncts. The problem ...
Gary M. Weiss, Haym Hirsh
AUSAI
2004
Springer
13 years 9 months ago
A Bayesian Metric for Evaluating Machine Learning Algorithms
How to assess the performance of machine learning algorithms is a problem of increasing interest and urgency as the data mining application of myriad algorithms grows. The standard...
Lucas R. Hope, Kevin B. Korb
CSB
2005
IEEE
143views Bioinformatics» more  CSB 2005»
13 years 10 months ago
Analysis of four different sets of predictive features for metalloproteins
Metals bound to the protein are important for functional or structural roles. Despite their importance there is a distinct lack of research for identification of metalloproteins f...
Huseyin Seker, Parvez I. Haris
AUSDM
2007
Springer
173views Data Mining» more  AUSDM 2007»
13 years 10 months ago
The Use of Various Data Mining and Feature Selection Methods in the Analysis of a Population Survey Dataset
This paper reports the results of feature reduction in the analysis of a population based dataset for which there were no specific target variables. All attributes were assessed a...
Ellen Pitt, Richi Nayak
KDD
2001
ACM
211views Data Mining» more  KDD 2001»
14 years 4 months ago
Magical thinking in data mining: lessons from CoIL challenge 2000
CoIL challenge 2000 was a supervised learning contest that attracted 43 entries. The authors of 29 entries later wrote explanations of their work. This paper discusses these repor...
Charles Elkan
KDD
2004
ACM
164views Data Mining» more  KDD 2004»
14 years 4 months ago
Cluster-based concept invention for statistical relational learning
We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Clustering improves...
Alexandrin Popescul, Lyle H. Ungar