A comparative experimental study of feature-weight learning approaches

Feature-weight learning (FWL) methods can be used to determine the importance degrees of each feature for constructing clusters or classifiers. In this paper, four FWL methods for unsupervised learning and two for supervised learning are surveyed. The FWL based models, i.e. feature-weighted fuzzy c-means (FWFCM) and feature-weighted support vector machine (FWSVM) are also reviewed. Through carefully selected experiments we find that FWFCM and FWSVM may improve the performances of their corresponding traditional fuzzy c-mean (FCM) and support vector machine (SVM), respectively. Moreover, the computational cost of FWL_Hung is least for unsupervised learning even though it may produce unsuitable feature weights in some extreme cases, while FWL_MI is most effective for supervised learning.