Kernelized Cost-Sensitive Listwise Ranking

This thesis research aims to conduct a study on a cost-sensitive listwise approach to learning to rank. Learning to Rank is an area of application in machine learning, typically supervised, to build ranking models for Information Retrieval systems. The training data consists of lists of items with some partial order specified induced by an ordinal score or a binary judgment (relevant/not relevant). The model purpose is to produce a permutation of the items in this list in a way which is close to the rankings in the training data. This technique has been successfully applied to ranking, and several approaches have been proposed since then, including the listwise approach. A cost-sensitive version of that is an adaptation of this framework which treats the documents within a list with different probabilities, i.e. attempt to impose weights for the documents with higher cost. We then take this algorithm to the next level by kernelizing the loss and exploring the optimization in different spaces. Among the different existing likelihood algorithms, we choose ListMLE as primary focus of experimentation, since it has been shown to be the approach with the best empirical performance. The theoretical framework is given along with its mathematical properties. Experimentation is done on the benchmark LETOR dataset. They contain queries and some characteristics of the retrieved documents and its human judgments on the relevance of the documents on the queries. Based on that we will show how the Kernel Cost-Sensitive ListMLE performs compared to the baseline Plain Cost-Sensitive ListMLE, ListNet, and RankSVM and show different aspects of the proposed loss function within different families of kernels

Ensemble of Example-Dependent Cost-Sensitive Decision Trees

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.Comment: 13 pages, 6 figures, Submitted for possible publicatio