Boosting decision stumps to do pairwise classification
Jun Xie, Lu Yu, Lei Zhu, Xue Hui · Electronics Letters · 2014
Pairwise classification is a task which predicts whether two samples belong to the same class or not. Boosting provides a way of combining many weak classifiers to produce a strong one and has been regarded as one of the most successful classification methodologies. The problem of pairwise classification is addressed by boosting decision stumps, the simplest weak classifier. Based on gentle AdaBoost, pairwise gentle AdaBoost of decision stumps is proposed to do pairwise classification. To make the classifier deal with a pair of inputs, sample‐weighted linear discriminant analysis (LDA) is proposed, which is tailored to boosting the framework. For pairwise classification, the proposed algorithm shows better performance than traditional boosting of decision stumps on two UCI data sets.