Evaluating Ensembles on QSAR Classification

• Novel, often quite technical algorithms, for ensembling artificial neural networks are constantly suggested. Naturally, when presenting a novel algorithm, the authors, at least implicitly, claim that their algorithm, in some aspect, represents the state-of-the-art. Obviously, the most important criterion is predictive performance, normally measured using either accuracy or area under the ROC-curve (AUC). This paper presents a study where the predictive performance of two widely acknowledged ensemble techniques; GASEN and NegBagg, is compared to more straightforward alternatives like bagging. The somewhat surprising result of the experimentation using, in total, 32 publicly available data sets from the medical domain, was that both GASEN and NegBagg were clearly outperformed by several of the straightforward techniques. One particularly striking result was that not applying the GASEN technique; i.e., ensembling all available networks instead of using the subset suggested by GASEN, turned out to produce more accurate ensembles.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Keyword

classification

ensembles

QSAR

Machine learning

data mining

Publication and Content Type

ref (subject category)

kon (subject category)