Feature subset selection using a new definition of classifiability
Pattern Recognition Letters
Lookahead-based algorithms for anytime induction of decision trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A fuzzy decision tree-based duration model for Standard Yorùbá text-to-speech synthesis
Computer Speech and Language
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
Automatic tuning of complex fuzzy systems with Xfuzzy
Fuzzy Sets and Systems
Moving towards efficient decision tree construction
Information Sciences: an International Journal
Behavioral-fusion control based on reinforcement learning
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Neuro-fuzzy systems with relation matrix
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Induced states in a decision tree constructed by Q-learning
Information Sciences: an International Journal
A survey of cost-sensitive decision tree induction algorithms
ACM Computing Surveys (CSUR)
Information Sciences: an International Journal
| Hi-index | 0.00 |
Decision tree induction is typically based on a top-down greedy algorithm that makes locally optimal decisions at each node. Due to the greedy and local nature of the decisions made at each node, there is considerable possibility of instances at the node being split along branches such that instances along some or all of the branches require a large number of additional nodes for classification. In this paper, we present a computationally efficient way of incorporating look-ahead into fuzzy decision tree induction. Our algorithm is based on establishing the decision at each internal node by jointly optimizing the node splitting criterion (information gain or gain ratio) and the classifiability of instances along each branch of the node. Simulations results confirm that the use of the proposed look-ahead method leads to smaller decision trees and as a consequence better test performance