Benchmarking attribute selection techniques for discrete class data mining
Top Cited Papers
- 17 November 2003
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Knowledge and Data Engineering
- Vol. 15 (6) , 1437-1447
- https://doi.org/10.1109/tkde.2003.1245283
Abstract
Data engineering is generally considered to be a central issue in the development of data mining applications. The success of many learning schemes, in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant, and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. Attribute selection generally involves a combination of search and attribute utility estimation plus evaluation with respect to specific learning schemes. This leads to a large number of possible permutations and has led to a situation where very few benchmark studies have been conducted. This paper presents a benchmark comparison of several attribute selection methods for supervised classification. All the methods produce an attribute ranking, a useful devise for isolating the individual merit of an attribute. Attribute selection is achieved by cross-validating the attribute rankings with respect to a classification learner to find the best attributes. Results are reported for a selection of standard data sets and two diverse learning schemes C4.5 and naive Bayes.Keywords
This publication has 6 references indexed in Scilit:
- Inductive learning algorithms and representations for text categorizationPublished by Association for Computing Machinery (ACM) ,1998
- Wrappers for feature subset selectionArtificial Intelligence, 1997
- Selection of relevant features and examples in machine learningArtificial Intelligence, 1997
- Feature selection for classificationIntelligent Data Analysis, 1997
- Learning Boolean concepts in the presence of many irrelevant featuresArtificial Intelligence, 1994
- A Practical Approach to Feature SelectionPublished by Elsevier ,1992