Lee, Jun Won Giraud-Carrier, Christophe 2024-01-20T12:02:20Z 2024-01-20T12:02:20Z 2022-01-25 2013-07 1088-467X https://pubs.kist.re.kr/handle/201004/127888 Classification algorithm selection is an open research problem whose solution has tremendous value for practitioners. In recent years, metalearning has emerged as a viable approach. Unfortunately, the ratio of examples to classes is small at the metalevel for any reasonable number of algorithms to choose from, and there are serious risks of overfitting due to underlying similarities among algorithms. To alleviate these problems, we propose to 1) cluster algorithms based on behavior similarity, and 2) redefine the metalearning task as mapping classification tasks to clusters of behaviorally-similar algorithms. Experiments with a wide range of classification tasks and algorithms demonstrate that the clustering-based selection model yields better results than typical selection models. English IOS PRESS Automatic selection of classification learning algorithms for data mining practitioners Article 10.3233/IDA-130599 1 INTELLIGENT DATA ANALYSIS, v.17, no.4, pp.665 - 678 INTELLIGENT DATA ANALYSIS 17 4 665 678 N scie scopus 000321623100007 2-s2.0-84881408928 Computer Science, Artificial Intelligence Computer Science Article ACCURACY RANKING Algorithm selection clustering metalearningg