An Iterative Growing and Pruning Algorithm for Classification Tree Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
A novel feature extraction method and hybrid tree classification for handwritten numeral recognition
Pattern Recognition Letters
Fuzzy classification trees for data analysis
Fuzzy Sets and Systems
The Sample Tree: A Sequential Hypothesis Testing Approach to 3D Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Fast Vector Matching Methods and Their Applications to Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Accelerating feature-vector matching using multiple-tree and sub-vector methods
Pattern Recognition
Data & Knowledge Engineering
Hi-index | 0.14 |
In the tree classifier with top-down search, a global decision is made via a series of local decisions. Although this approach gains in classification efficiency, it also gives rise to error accumulation which can be very harmful when the number of classes is very large. To overcome this difficulty, a new tree classifier with the following characteristics is proposed: 1) fuzzy logic search is used to find all ``possible correct classes,'' and some similarity measures are used to determine the ``most probable class''; 2) global training is applied to generate extended terminals in order to enhance the recognition rate; 3) both the training and search algorithms have been given a lot of flexibility, to provide tradeoffs between error and rejection rates, and between the recognition rate and speed. A computer simulation of the decision trees for the recognition of 3200 Chinese character categories yielded a very high recognition rate of 99.93 percent and a very high speed of 861 samples/s, when the program was written in a high level language and run on a large multiuser time-sharing computer.