Instance-Based Learning Algorithms Export

@article{aha91a, abstract = {Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several realworld databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.}, author = {Aha, David W. and Kibler, Dennis and Albert, Marc K.}, citeulike-article-id = {1585703}, doi = {10.1023/A:1022689900470}, journal = {Machine Learning}, keywords = {ai, bibtex-import, learning}, number = {1}, pages = {33--66}, posted-at = {2007-08-23 14:42:24}, priority = {2}, title = {Instance-Based Learning Algorithms}, volume = {6}, year = {1991} }

TY - JOUR ID - aha91a L3 - citeulike-article-id:1585703 N2 - Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several realworld databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm. IS - 1 JF - Machine Learning EP - 66 TI - Instance-Based Learning Algorithms VL - 6 SP - 33 KW - ai KW - bibtex-import KW - learning AU - Aha, David AU - Kibler, Dennis AU - Albert, Marc PY - 1991/// ER -