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An efficient algorithm for the incremental construction of a piecewise linear classifier
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DOI：10.1016/j.is.2010.12.002.png)
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In this paper the problem of finding piecewise linear boundaries between sets is considered and is applied for solving supervised data classification problems. An algorithm for the computation of piecewise linear boundaries, consisting of two main steps, is proposed. In the first step sets are approximated by hyperboxes to find so-called indeterminate regions between sets. In the second step sets are separated inside these indeterminate regions by piecewise linear functions. These functions are computed incrementally starting with a linear function. Results of numerical experiments are reported. These results demonstrate that the new algorithm requires a reasonable training time and it produces consistently good test set accuracy on most data sets comparing with mainstream classifiers. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Data mining
Data classification
Supervised learning
Artificial intelligence
Knowledge-based systems
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