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Semi-supervised Decision Tree

A Semi-supervised Decision Tree extends standard decision tree induction — such as CART or C4.5 — to exploit unlabeled observations alongside the labeled training set. By iteratively assigning tentative labels to unlabeled data and incorporating them into the growing or splitting process, the algorithm can achieve better accuracy than a fully supervised tree trained on the labeled subset alone, which is especially valuable when labeling is expensive or time-consuming.

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Semi-supervised Decision Tree Learning

Taksonoomiline meetodikirje · ml-model / machine-learning

Levin, E. & Shapiro, E. (2000). Learning Decision Trees from Semi-labeled Examples. Proceedings of the ICML Workshop on Attribute-Value and Relational Learning. · URL
Zhu, X. & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers. · ISBN 978-1-598-29548-9

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