Name
Abstract | ||
|---|---|---|
We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of switching circuits. To cope with high time complexity of finding an optimal decomposition, we propose a suboptimal heuristic algorithm. The method, implemented in program HINT(Hierarchy INduction Tool), is experimentally evaluated using a set of artificial and real-world learning problems. In particular, the evaluation addresses the generalization property of decomposition and its capability to discover meaningful hierarchies. The experiments show that HINT performs well in both respects. (C) 1999 Elsevier Science B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0004-3702(99)00008-9 | Artif. Intell. |
Keywords | Field | DocType |
heuristic algorithm,boolean function,generalization,time complexity,machine learning,function decomposition,functional decomposition | Boolean function,Constructive induction,Concept class,Heuristic (computer science),Functional decomposition,Artificial intelligence,Hierarchy,Time complexity,Machine learning,Mathematics,Complex problems | Journal |
Volume | Issue | ISSN |
109 | 1-2 | 0004-3702 |
Citations | PageRank | References |
35 | 3.57 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Blaz Zupan | 1 | 1277 | 102.37 |
| Marko Bohanec | 2 | 334 | 48.69 |
| Janez Demsar | 3 | 35 | 3.57 |
| Ivan Bratko | 4 | 1526 | 405.03 |