Training a reciprocal-sigmoid classifier by feature scaling-space

摘要

This paper presents a reciprocal-sigmoid model for pattern classification. This proposed classifier can be considered as a Φ-machine since it preserves the theoretical advantage of linear machines where the weight parameters can be estimated in a single step. The model can also be considered as an approximation to logistic regression under the framework of Generalized Linear Models. While inheriting the necessary classification capability from logistic regression, the problems of local minima and tedious recursive search no longer exist in the proposed formulation. To handle possible over-fitting when using high order models, the classifier is trained using multiple samples of uniformly scaled pattern features. Empirically, the classifier is evaluated using a benchmark synthetic data from random sampling runs for initial statistical evidence regarding its classification accuracy and computational efficiency. Additional experiments based on ten runs of 10-fold cross validations on 40 data sets further support the effectiveness of the reciprocal-sigmoid model, where its classification accuracy is seen to be comparable to several top classifiers in the literature. Main reasons for the good performance are attributed to effective use of reciprocal sigmoid for embedding nonlinearities and effective use of bundled feature sets for smoothing the training error hyper-surface.