What question did this study set out to answer?

To develop a nonparametric algorithm for hierarchical partitioning of feature space using mutual information.

July 1, 1982

Hierarchical Classifier Design Using Mutual Information

Key Points

To develop a nonparametric algorithm for hierarchical partitioning of feature space using mutual information.
Developed a nonparametric algorithm based on average mutual information for hierarchical partitioning.
Generated an efficient partitioning tree to minimize the probability of error.
Demonstrated the algorithm with examples including handprinted numeral recognition.
The algorithm effectively maximized average mutual information gain at each partitioning step.
Generated a confidence bound expression for the classifier's performance.
Showed practical effectiveness in multifeature multicategory pattern recognition problems, including numeral recognition.

Abstract

A nonparametric algorithm is presented for the hierarchical partitioning of the feature space. The algorithm is based on the concept of average mutual information, and is suitable for multifeature multicategory pattern recognition problems. The algorithm generates an efficient partitioning tree for specified probability of error by maximizing the amount of average mutual information gain at each partitioning step. A confidence bound expression is presented for the resulting classifier. Three examples, including one of handprinted numeral recognition, are presented to demonstrate the effectiveness of the algorithm.

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Cite This Study

Sethi et al. (Thu,) studied this question.

synapsesocial.com/papers/6a07edc47ad161a3abfe0a51 https://doi.org/https://doi.org/10.1109/tpami.1982.4767278

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