The Strength of Weak Learnability
Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
HOT: Heuristics for Oblique Trees
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Linear Machine Decision Trees
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
A Nonparametric Partitioning Procedure for Pattern Classification
IEEE Transactions on Computers
Using Genetic Programming for the Induction of Oblique Decision Trees
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
A Geometric Algorithm for Learning Oblique Decision Trees
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
A scalable decision tree system and its application in pattern recognition and intrusion detection
Decision Support Systems
CSNL: A cost-sensitive non-linear decision tree algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
New algorithms for learning and pruning oblique decision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A connectionist approach to generating oblique decision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 12.05 |
Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts. One disadvantage of univariate decision trees is that they produce complex and inaccurate models when decision boundaries are not orthogonal to axes. In this paper we introduce the Fisher's Tree, it is a classifier that takes advantage of dimensionality reduction of Fisher's linear discriminant and uses the decomposition strategy of decision trees, to come up with an oblique decision tree. Our proposal generates an artificial attribute that is used to split the data in a recursive way. The Fisher's decision tree induces oblique trees whose accuracy, size, number of leaves and training time are competitive with respect to other decision trees reported in the literature. We use more than ten public available data sets to demonstrate the effectiveness of our method.