Abstract
To compute a partition quality for a decision tree, we propose a new measure called NIM “New Information Measure”. The measure is simpler, provides similar performance, and sometimes outperforms the existing measures used with tree-based methods. The experimental results using the MONITDIAB application (Taleb & Atmani, 2013) and datasets from the UCI repository (Asuncion & Newman, 2007) confirm the classification capabilities of our proposal in comparison to the Shannon measure used with ID3 and C4.5 decision tree methods.
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