In this paper, we propose advanced text analytics and costsensitive classification-based approaches for call quality monitoring and show that automatic quality monitoring with ASR transcripts can be achieved with a high accuracy. Our system analyzes ASR transcripts and determines if a call is a good call or a bad call. The set of features were identified through analysis of a large number of hu...

A class of problems between classification and regression, learning to predict ordinal classes, has not received much attention so far, even though there are many problems in the real world that fall into that category. Given ordered classes, one is not only interested in maximizing the classification accuracy, but also in minimizing the distances between the actual and the predicted classes. T...

Computational linguistics covers the statistical and logical modeling of languages using computer-based software-hardware tools. An important component in CL systems is the morphological parser. The scope of our study is to build a statistical method to learn the rules of word inflection. The pre-requirement regarding the language is that the language uses words which are sequences of character...

A lot of approaches, each following a different strategy, have been proposed in the literature to provide AdaBoost with cost-sensitive properties. In the first part of this series of two papers, we have presented these algorithms in a homogeneous notational framework, proposed a clustering scheme for them and performed a thorough theoretical analysis of those approaches with a fully theoretical...

A lot of practical machine learning applications deal with interactive classification problems where trained classifiers are used to help humans find positive examples that are of interest to them. Typically, these classifiers label a large number of test examples and present the humans with a ranked list to review. The humans involved in this process are often expensive domain experts with lim...

Knowledge of human presence and interaction in a vehicle is of growing interest to vehicle manufacturers for design and safety purposes. We present a framework to perform the tasks of occupant detection and occupant classification for automatic child locks and airbag suppression. It operates for all passenger seats, using a single overhead camera. A transfer learning technique is introduced to ...

We study multi-label classification (MLC) with three important real-world issues: online updating, label space dimensional reduction (LSDR), and cost-sensitivity. Current MLC algorithms have not been designed to address these three issues simultaneously. In this paper, we propose a novel algorithm, cost-sensitive dynamic principal projection (CS-DPP) that resolves all three issues. The foundati...

This paper is intended to verify that cost-sensitive learning is a competitive approach for learning fuzzy rules in certain imbalanced classification problems. It will be shown that there exist cost matrices whose use in combination with a suitable classifier allows for improving the results of some popular data-level techniques. The well known FURIA algorithm is extended to take advantage of t...

Linear classification has been widely used in many high-dimensional applications like text classification. To perform linear classification for large-scale tasks, we often need to design distributed learning methods on a cluster of multiple machines. In this paper, we propose a new distributed learning method, called featuredistributed stochastic variance reduced gradient (FD-SVRG) for high-dim...

In this paper, we perform an empirical study of the impact of noise on cost-sensitive (CS) learning, through observations on how a CS learner reacts to the mislabeled training examples in terms of misclassification cost and classification accuracy. Our empirical results and theoretical analysis indicate that mislabeled training examples can raise serious concerns for cost-sensitive classificati...