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IJCAI
1989
13 years 9 months ago
An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods
Classification methods from statistical pattern recognition, neural nets, and machine learning were applied to four real-world data sets. Each of these data sets has been previous...
Sholom M. Weiss, Ioannis Kapouleas
SIGIR
2006
ACM
14 years 2 months ago
Feature diversity in cluster ensembles for robust document clustering
The performance of document clustering systems depends on employing optimal text representations, which are not only difficult to determine beforehand, but also may vary from one ...
Xavier Sevillano, Germán Cobo, Francesc Al&...
KDD
2004
ACM
151views Data Mining» more  KDD 2004»
14 years 9 months ago
Feature selection in scientific applications
Numerous applications of data mining to scientific data involve the induction of a classification model. In many cases, the collection of data is not performed with this task in m...
Erick Cantú-Paz, Shawn Newsam, Chandrika Ka...
BMCBI
2004
205views more  BMCBI 2004»
13 years 8 months ago
A combinational feature selection and ensemble neural network method for classification of gene expression data
Background: Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for...
Bing Liu, Qinghua Cui, Tianzi Jiang, Songde Ma
IDEAS
2007
IEEE
71views Database» more  IDEAS 2007»
14 years 2 months ago
Feature Space Enrichment by Incorporation of Implicit Features for Effective Classification
Feature Space Conversion for classifiers is the process by which the data that is to be fed into the classifier is transformed from one form to another. The motivation behind doin...
Abhishek Srivastava, Osmar R. Zaïane, Maria-L...