%0 Conference Paper %1 Webb93a %A Webb, G. I. %B Proceedings of the Sixth Australian Joint Conference on Artificial Intelligence (AI'93) %C Singapore %D 1993 %E Rowles, C. %E Liu, H. %E Foo, N. %I World Scientific %K Association Discovery Learning, OPUS, Rule Search, %P 342-347 %T Systematic Search for Categorical Attribute-Value Data-Driven Machine Learning %X Optimal Pruning for Unordered Search is a search algorithm that enables complete search through the space of possible disjuncts at the inner level of a covering algorithm. This algorithm takes as inputs an evaluation function, e, a training set, t, and a set of specialisation operators, o. It outputs a set of operators from o that creates a classifier that maximises e with respect to t. While OPUS has exponential worst case time complexity, the algorithm is demonstrated to reach solutions for complex real world domains within reasonable time frames. Indeed, for some domains, the algorithm exhibits greater computational efficiency than common heuristic search algorithms.

@inproceedings{Webb93a, abstract = {Optimal Pruning for Unordered Search is a search algorithm that enables complete search through the space of possible disjuncts at the inner level of a covering algorithm. This algorithm takes as inputs an evaluation function, e, a training set, t, and a set of specialisation operators, o. It outputs a set of operators from o that creates a classifier that maximises e with respect to t. While OPUS has exponential worst case time complexity, the algorithm is demonstrated to reach solutions for complex real world domains within reasonable time frames. Indeed, for some domains, the algorithm exhibits greater computational efficiency than common heuristic search algorithms.}, added-at = {2016-03-20T05:42:04.000+0100}, address = {Singapore}, audit-trail = {*}, author = {Webb, G. I.}, biburl = {https://www.bibsonomy.org/bibtex/2e7ac8ca97c33bfacba4d29a05196a3f1/giwebb}, booktitle = {Proceedings of the Sixth Australian Joint Conference on Artificial Intelligence (AI'93)}, editor = {Rowles, C. and Liu, H. and Foo, N.}, interhash = {99672b41e85ba6a86e4b7b146c69fa5a}, intrahash = {e7ac8ca97c33bfacba4d29a05196a3f1}, keywords = {Association Discovery Learning, OPUS, Rule Search,}, location = {Melbourne, Australia}, pages = {342-347}, publisher = {World Scientific}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Systematic Search for Categorical Attribute-Value Data-Driven Machine Learning}, year = 1993 }