D. Wettschereck et al., A REVIEW AND EMPIRICAL-EVALUATION OF FEATURE WEIGHTING METHODS FOR A CLASS OF LAZY LEARNING ALGORITHMS, Artificial intelligence review, 11(1-5), 1997, pp. 273-314
Citations number
104
Categorie Soggetti
Computer Sciences, Special Topics","Computer Science Artificial Intelligence
Many lazy learning algorithms are derivatives of the k-nearest neighbo
r (Ic-NN) classifier, which uses a distance function to generate predi
ctions from stored instances. Several studies have shown that K-NN's p
erformance is highly sensitive to the definition of its distance funct
ion. Many K-NN variants have been proposed to reduce this sensitivity
by parameterizing the distance function with feature weights. However,
these variants have not been categorized nor empirically compared. Th
is paper reviews a class of weight-setting methods for lazy learning a
lgorithms. We introduce a framework for distinguishing these methods a
nd empirically compare them. We observed four trends from our experime
nts and conducted further studies to highlight them. Our results sugge
st that methods which use performance feedback to assign weight settin
gs demonstrated three advantages over other methods: they require less
pre-processing, perform better in the presence of interacting feature
s, and generally require less training data to learn good settings. We
also found that continuous weighting methods tend to outperform featu
re selection algorithms for tasks where some features are useful but l
ess important than others.