Ensemble of Linear Experts as an Interpretable Piecewise-linear Classifier
نویسنده
چکیده
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 competitive 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 module to obtain a linear rule within the allocated sub-space. The linearity of the ensemble’s module significantly simplifies the rule extraction process.
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