A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, the subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed. The relation between decision trees and neutral networks (NN) is also discussed. >

/pdf/a-survey-of-decision-tree-classifier-methodology-3kolfz0lgh.pdf

A survey of decision tree classifier methodology

Possible solutions to the problem of combining classifiers can be divided into three categories according to the levels of information available from the various classifiers. Four approaches based on different methodologies are proposed for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-nearest-neighbor, and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly. For example, on the US zipcode database, 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as high reliability with 95% recognition, 0% substitution, and 5% rejection. >

Methods of combining multiple classifiers and their applications to handwriting recognition

An efficient iterative method is proposed to grow and prune classification trees. This method divides the data sample into two subsets and iteratively grows a tree with one subset and prunes it with the other subset, successively interchanging the roles of the two subsets. The convergence and other properties of the algorithm are established. Theoretical and practical considerations suggest that the iterative tree growing and pruning algorithm should perform better and require less computation than other widely used tree growing and pruning algorithms. Numerical results on a waveform recognition problem are presented to support this view. >

An iterative growing and pruning algorithm for classification tree design

A test sequence is used to select the best rule from a class of discrimination rules defined in terms of the training sequence. The Vapnik-Chervonenkis and related inequalities are used to obtain distribution-free bounds on the difference between the probability of error of the selected rule and the probability of error of the best rule in the given class. The bounds are used to prove the consistency and asymptotic optimality for several popular classes, including linear discriminators, nearest-neighbor rules, kernel-based rules, histogram rules, binary tree classifiers, and Fourier series classifiers. In particular, the method can be used to choose the smoothing parameter in kernel-based rules, to choose k in the k-nearest neighbor rule, and to choose between parametric and nonparametric rules. >

/pdf/automatic-pattern-recognition-a-study-of-the-probability-of-4y1ojlau19.pdf

Automatic pattern recognition: a study of the probability of error

Based on a recursive process of reducing the entropy, the general decision tree classifier with overlap has been analyzed. Several theorems have been proposed and proved. When the number of pattern classes is very large, the theorems can reveal both the advantages of a tree classifier and the main difficulties in its implementation. Suppose H is Shannon's entropy measure of the given problem. The theoretical results indicate that the tree searching time can be minimized to the order O(H), but the error rate is also in the same order O(H) due to error accumulation. However, the memory requirement is in the order 0(H exp(H)) which poses serious problems in the implementation of a tree classifier for a large number of classes. To solve these problems, several theorems related to the bounds on the search time, error rate, memory requirement and overlap factor in the design of a decision tree have been proposed and some principles have been established to analyze the behaviors of the decision tree. When applied to classify sets of 64, 450, and 3200 Chinese characters, respectively, the experimental results support the theoretical predictions. For 3200 classes, a very high recognition rate of 99.88 percent was achieved at a high speed of 873 samples/s when the experiment was conducted on a Cyber 172 computer using a high-level language.

Analysis and Design of a Decision Tree Based on Entropy Reduction and Its Application to Large Character Set Recognition

/pdf/an-automated-approach-to-the-design-of-decision-tree-52imn6efuf.pdf

An Automated Approach to the Design of Decision Tree Classifiers

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An Automated Approach to the Design of Decision Tree Classifiers