Many data sets show significant correlations between input variables, and much useful information is hidden in the data in a non- linear format. It has been shown that a neural network is better than a direct application of induction trees in modeling nonlinear characteristics of sample data. We have extracted a compact set of rules to support data with input variable relations over continuous-valued attributes. Those re- lations as a set of linear classifiers can be obtained from neural network modeling based on back-propagation. It is shown in this paper that vari- able thresholds play an important role in constructing linear classifier rules when we use a decision tree over linear classifiers extracted from a multilayer perceptron. We have tested this scheme over several data sets to compare it with the decision tree results.
|Title of host publication||Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings|
|Editors||David Cheung, Graham J. Williams, Qing Li|
|Number of pages||12|
|ISBN (Print)||3540419101, 9783540419105|
|Publication status||Published - 2001|
|Event||5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 - Kowloon, Hong Kong|
Duration: 2001 Apr 16 → 2001 Apr 18
|Name||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
|Other||5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001|
|Period||01/4/16 → 01/4/18|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)