Online adaptive decision trees.

Jayanta Basak

doi:10.1162/0899766041336396

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PubMed · 15265330

Online adaptive decision trees.

Jayanta Basak

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Abstract

Decision trees and neural networks are widely used tools for pattern classification. Decision trees provide highly localized representation, whereas neural networks provide a distributed but compact representation of the decision space. Decision trees cannot be induced in the online mode, and they are not adaptive to changing environment, whereas neural networks are inherently capable of online learning and adpativity. Here we provide a classification scheme called online adaptive decision trees (OADT), which is a tree-structured network like the decision trees and capable of online learning like neural networks. A new objective measure is derived for supervised learning with OADT. Experimental results validate the effectiveness of the proposed classification scheme. Also, with certain real-life data sets, we find that OADT performs better than two widely used models: the hierarchical mixture of experts and multilayer perceptron.

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Computer Simulation Decision Making, Computer-Assisted Decision Trees Discriminant Analysis Humans Learning Models, Neurological Neural Networks, Computer Online Systems Signal Processing, Computer-Assisted Statistics, Nonparametric Comparative Study Journal Article

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Jayanta Basak. 2004. Online adaptive decision trees.. https://doi.org/10.1162/0899766041336396

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