C4.5: programs for machine learning
C4.5: programs for machine learning
Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Weighted Majority Decision among Region Rules for a Categorical Dataset
DS '99 Proceedings of the Second International Conference on Discovery Science
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Weighted Majority Decision among Region Rules for a Categorical Dataset
DS '99 Proceedings of the Second International Conference on Discovery Science
| Hi-index | 0.00 |
We consider the classification problem of how to predict the values of a categorical attribute of interest using the other numerical attributes in a given set of tuples. Decision by voting such as bagging and boosting attempts to enhance the existing classification techniques like decision trees by using a majority decision among them. However, a high accuracy ratio of prediction sometimes requires complicated predictors, and makes it hard to understand the simple laws affecting the values of the attribute of interest. We instead consider another approach of using of at most several fairly simple voters that can compete with complex prediction tools. We pursue this idea to handle numeric datasets and employ region splitting rules as relatively simple voters. The results of empirical tests show that the accuracy of decision by several voters is comparable to that of decision trees, and the computational cost is inexpensive.