Ensemble of Linear Experts as an Interpretable Piecewise Linear Classifier
Year: 2008
Type: article
Abstract: . In this study we propose a new ensemble model composed of several linear perceptrons. The objective of this study is to build a piecewise-linear classifier that is not only competitive to Multilayer Perceptrons(MLP) in generalization performance but also interpretable in the form of human-comprehensible rules. We present a simple competi-tive training method that allows the ensemble to effectively divide a given training space into several sub-spaces on the basis of so called ”confidence value”, and train each mod-ule to obtain a linear rule within the allocated sub-space. The linearity of the ensemble’s module significantly simplifies the rule extraction process.
Author: Pitoyo Hartono
Institution: Future University Hakodate
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2149434756