Multiple binary decision tree classifiers

✍ Scribed by Seymour Shlien

• Publisher: Elsevier Science
• Year: 1990
• Tongue: English
• Weight: 565 KB
• Volume: 23
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/0031-3203(90)90098-6

✦ Synopsis

AMtract--Binary decision trees based on nonparametric statistical models of the data provide a solution to difficult decision problems where there are many classes and many available features related in a complex manner. Unfortunately, the technique requires a very large training set and is often limited by the size of the training set rather than by the discriminatory power of the feature. This paper demonstrates that higher classification accuracies can be obtained from the same training set by using a combination of decision trees and by reaching a consensus using Dempster and Shafer's theory of evidence.

Pattern recognition

Tree classifiers Character recognition Dempster-Shafer decision theory Minimum entropy Misclassification error