One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

CogBoost: Boosting for Fast Cost-Sensitive Graph Classification

© 2015 IEEE. Graph classification has drawn great interests in recent years due to the increasing number of applications involving objects with complex structure relationships. To date, all existing graph classification algorithms assume, explicitly or implicitly, that misclassifying instances in different classes incurs an equal amount of cost (or risk), which is often not the case in real-life applications (where misclassifying a certain class of samples, such as diseased patients, is subject to more expensive costs than others). Although cost-sensitive learning has been extensively studied, all methods are based on data with instance-feature representation. Graphs, however, do not have features available for learning and the feature space of graph data is likely infinite and needs to be carefully explored in order to favor classes with a higher cost. In this paper, we propose, CogBoost, a fast cost-sensitive graph classification algorithm, which aims to minimize the misclassification costs (instead of the errors) and achieve fast learning speed for large scale graph data sets. To minimize the misclassification costs, CogBoost iteratively selects the most discriminative subgraph by considering costs of different classes, and then solves a linear programming problem in each iteration by using Bayes decision rule based optimal loss function. In addition, a cutting plane algorithm is derived to speed up the solving of linear programs for fast learning on large scale data sets. Experiments and comparisons on real-world large graph data sets demonstrate the effectiveness and the efficiency of our algorithm

Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies

Cost-sensitive classification based on Bregman divergences

The main object of this PhD. Thesis is the identification, characterization and study of new loss functions to address the so-called cost-sensitive classification. Many decision problems are intrinsically cost-sensitive. However, the dominating preference for cost-insensitive methods in the machine learning literature is a natural consequence of the fact that true costs in real applications are di fficult to evaluate. Since, in general, uncovering the correct class of the data is less costly than any decision error, designing low error decision systems is a reasonable (but suboptimal) approach. For instance, consider the classification of credit applicants as either being good customers (will pay back the credit) or bad customers (will fail to pay o part of the credit). The cost of classifying one risky borrower as good could be much higher than the cost of classifying a potentially good customer as bad. Our proposal relies on Bayes decision theory where the goal is to assign instances to the class with minimum expected cost. The decision is made involving both costs and posterior probabilities of the classes. Obtaining calibrated probability estimates at the classifier output requires a suitable learning machine, a large enough representative data set as well as an adequate loss function to be minimized during learning. The design of the loss function can be aided by the costs: classical decision theory shows that cost matrices de ne class boundaries determined by posterior class probability estimates. Strictly speaking, in order to make optimal decisions, accurate probability estimates are only required near the decision boundaries. It is key to point out that the election of the loss function becomes especially relevant when the prior knowledge about the problem is limited or the available training examples are somehow unsuitable. In those cases, different loss functions lead to dramatically different posterior probabilities estimates. We focus our study on the set of Bregman divergences. These divergences offer a rich family of proper losses that has recently become very popular in the machine learning community [Nock and Nielsen, 2009, Reid and Williamson, 2009a]. The first part of the Thesis deals with the development of a novel parametric family of multiclass Bregman divergences which captures the information in the cost matrix, so that the loss function is adapted to each specific problem. Multiclass costsensitive learning is one of the main challenges in cost-sensitive learning and, through this parametric family, we provide a natural framework to successfully overcome binary tasks. Following this idea, two lines are explored: Cost-sensitive supervised classification: We derive several asymptotic results. The first analysis guarantees that the proposed Bregman divergence has maximum sensitivity to changes at probability vectors near the decision regions. Further analysis shows that the optimization of this Bregman divergence becomes equivalent to minimizing the overall cost regret in non-separable problems, and to maximizing a margin in separable problems. Cost-sensitive semi-supervised classification: When labeled data is scarce but unlabeled data is widely available, semi-supervised learning is an useful tool to make the most of the unlabeled data. We discuss an optimization problem relying on the minimization of our parametric family of Bregman divergences, using both labeled and unlabeled data, based on what is called the Entropy Minimization principle. We propose the rst multiclass cost-sensitive semi-supervised algorithm, under the assumption that inter-class separation is stronger than intra-class separation. The second part of the Thesis deals with the transformation of this parametric family of Bregman divergences into a sequence of Bregman divergences. Work along this line can be further divided into two additional areas: Foundations of sequences of Bregman divergences: We generalize some previous results about the design and characterization of Bregman divergences that are suitable for learning and their relationship with convexity. In addition, we aim to broaden the subset of Bregman divergences that are interesting for cost-sensitive learning. Under very general conditions, we nd sequences of (cost-sensitive) Bregman divergences, whose minimization provides minimum (cost-sensitive) risk for non-separable problems and some type of maximum margin classifiers in separable cases. Learning with example-dependent costs: A strong assumption is widespread through most cost-sensitive learning algorithms: misclassification costs are the same for all examples. In many cases this statement is not true. We claim that using the example-dependent costs directly is more natural and will lead to the production of more accurate classifiers. For these reasons, we consider the extension of cost-sensitive sequences of Bregman losses to example-dependent cost scenarios to generate finely tuned posterior probability estimates

SVM-Based Negative Data Mining to Binary Classification

The properties of training data set such as size, distribution and the number of attributes significantly contribute to the generalization error of a learning machine. A not well-distributed data set is prone to lead to a partial overfitting model. Two approaches proposed in this dissertation for the binary classification enhance useful data information by mining negative data. First, an error driven compensating hypothesis approach is based on Support Vector Machines (SVMs) with (1+k)-iteration learning, where the base learning hypothesis is iteratively compensated k times. This approach produces a new hypothesis on the new data set in which each label is a transformation of the label from the negative data set, further producing the positive and negative child data subsets in subsequent iterations. This procedure refines the base hypothesis by the k child hypotheses created in k iterations. A prediction method is also proposed to trace the relationship between negative subsets and testing data set by a vector similarity technique. Second, a statistical negative example learning approach based on theoretical analysis improves the performance of the base learning algorithm learner by creating one or two additional hypotheses audit and booster to mine the negative examples output from the learner. The learner employs a regular Support Vector Machine to classify main examples and recognize which examples are negative. The audit works on the negative training data created by learner to predict whether an instance is negative. However, the boosting learning booster is applied when audit does not have enough accuracy to judge learner correctly. Booster works on training data subsets with which learner and audit do not agree. The classifier for testing is the combination of learner, audit and booster. The classifier for testing a specific instance returns the learner\u27s result if audit acknowledges learner\u27s result or learner agrees with audit\u27s judgment, otherwise returns the booster\u27s result. The error of the classifier is decreased to O(e^2) comparing to the error O(e) of a base learning algorithm