International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
An incremental method for finding multivariate splits for decision trees
Proceedings of the seventh international conference (1990) on Machine learning
Machine Learning
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Meta Analysis of Classification Algorithms for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Applying classification algorithms in practice
Statistics and Computing
Color models for outdoor machine vision
Computer Vision and Image Understanding
Improved Rooftop Detection in Aerial Images with Machine Learning
Machine Learning
Hybrid approaches for classification under information acquisition cost constraint
Decision Support Systems
An application of machine learning and statistics to defect detection
Intelligent Data Analysis
Induction of multiclass multifeature split decision trees from distributed data
Pattern Recognition
Hybrid approaches for classification under information acquisition cost constraint
Decision Support Systems
CSNL: A cost-sensitive non-linear decision tree algorithm
ACM Transactions on Knowledge Discovery from Data (TKDD)
A cost-sensitive decision tree approach for fraud detection
Expert Systems with Applications: An International Journal
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Recent work in feature-based classification has focused on nonparametric techniques that can classify instances even when the underlying feature distributions are unknown. The inference algorithms for training these techniques, however, are designed to maximize the accuracy of the classifier, with all errors weighted equally. In many applications, certain errors are far more costly than others, and the need arises for nonparametric classification techniques that can be trained to optimize task-specific cost functions. This correspondence reviews the linear machine decision tree (LMDT) algorithm for inducing multivariate decision trees, and shows how LMDT can be altered to induce decision trees that minimize arbitrary misclassification cost functions (MCF's). Demonstrations of pixel classification in outdoor scenes show how MCF's can optimize the performance of embedded classifiers within the context of larger image understanding systems.