Recursive estimation and time-series analysis: an introduction
Recursive estimation and time-series analysis: an introduction
Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Machine Learning
ACM Computing Surveys (CSUR)
Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Expert Systems with Applications: An International Journal
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This paper presents a new method (HOT) of generating oblique decision trees. Oblique trees have been shown to be useful tools for classification in some problem domains, producing accurate and intuitive solutions. Our method can be incorporated into a variety of existing decision tree tools and the paper illustrates this with two very distinct tree generators. The key idea is a method of learning oblique vectors and using the corresponding families of hyperplanes orthogonal to these vectors to separate regions with distinct dominant classes. Experimental results indicate that the learnt oblique hyperplanes lead to compact and accurate oblique trees. HOT is simple and easy to incorporate into most decision tree packages, yet its results compare well with much more complex schemes for generating oblique trees.