Lee, Jun Won Giraud-Carrier, Christophe 2024-01-20T10:31:40Z 2024-01-20T10:31:40Z 2022-01-25 2014-02 1088-467X https://pubs.kist.re.kr/handle/201004/127118 We illustrate the danger of using default implementations of learning algorithms by showing that the implementation of RBF networks in the three most popular open source data mining software packages causes the algorithm to behave and perform like naive Bayes in most instances. This result has significant implications for both practitioners and researchers in terms of computational complexity, ensemble design and metalearning for algorithm selection. We outline the limits of the similarity between RBF and naive Bayes, and use metalearning to build a selection model capable of accurately discriminating between the two algorithms, so that extra computation is only incurred when it is likely to produce significant improvement in predictive accuracy. English IOS PRESS On the dangers of default implementations: The case of radial basis function networks Article 10.3233/IDA-140640 1 INTELLIGENT DATA ANALYSIS, v.18, no.2, pp.261 - 279 INTELLIGENT DATA ANALYSIS 18 2 261 279 N scie scopus 000332477600009 2-s2.0-84900663985 Computer Science, Artificial Intelligence Computer Science Article FUNCTION NEURAL-NETWORKS TIME CLASSIFICATION PREDICTION ACCURACY FACE Algorithm analysis naive Bayes RBF networks metalearning