Name
Abstract | ||
|---|---|---|
As the exponential increase of data in the world, machine learning, pattern recognition, data mining etc. are attracting more attentions recently. Classification is one of the major research in pattern recognition and a large number of methods have been proposed such as decision trees, neural networks (NNs), support vector machines (SVMs). In order to easily understand and analyze the reason of the classification results, decision trees are useful comparing to NNs and SVMs. In this paper, to enhance the classification ability of decision trees, a new evolutionary algorithm for creating decision graphs is proposed as a superset of decision trees, where multi-root nodes and majority voting mechanism based on Maximum a posteriori are introduced. In the performance evaluation, it is clarified that the proposed method shows better classification ability than decision trees. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.2991/jrnal.2016.3.1.11 | JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE |
Keywords | Field | DocType |
evolutionary computation,decision graph,classification,majority vote,multi root nodes | Graph,Evolutionary algorithm,Evolutionary computation,Artificial intelligence,Majority rule,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
3 | 1 | 2352-6386 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shingo Mabu | 1 | 493 | 77.00 |
| Masanao Obayashi | 2 | 198 | 26.10 |
| Takashi Kuremoto | 3 | 196 | 27.73 |