Support Vector Machine has shown to have good performance in many practical classification settings. In this paper we propose, for multi-group classification, a biobjective optimization model in which we consider not only the generalization ability (modelled through the margin maximization), but also costs associated with the features. This cost is not limited to an economical payment, but can ...

We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. Our approach works by reducing fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classi...

Software Component Reuse has applied to a wide spectrum of software development. Software Component Reuse provide cost, time saving, etc. with increased product quality and decreased development cost. The component based approach involves constructing the application from different kinds of components store in reusable repositories. To retrieve the exact component from reusable repositories is ...

We study surrogate losses in the context of cost-sensitive classification with example-dependent costs, a problem also known as regression level set estimation. We give sufficient conditions on the surrogate loss for the existence of a surrogate regret bound. Such bounds imply that as the surrogate risk tends to its optimal value, so too does the expected misclassification cost. These kinds of ...

It was not until 2010 when businesses, politicians and people in general began to realise the potential of Twitter in Spain. This fact has awoken research interest in the extraction of knowledge from Twitter. This paper aims to fill the gap of the lack of resources for Twitter sentiment analysis in Spanish by performing a study of different features and machine learning algorithms for classifyi...

A generic, hierarchical, and multifidelity unit cost of acquisition estimating methodology for outside production machined parts is presented. The originality of the work lies with the method's inherent capability of being able to generate multilevel and multifidelity cost relations for large volumes of parts utilizing process, supply chain costing data, and varying degrees of part design defin...

Recently, cost-based Autonomous Response System (ARS) proposals are based on intrusion detection analysis. However, the implementation of the analysis in multiclass classification-based ARS potentially leads to a wrong response action set decision. This is because the analysis may produces irrelevant response value, as it is not considering the false possibility in a true positive condition. In...

Some classification problems, such as the NTCIR06 F-term patent classification task, have general or specific relations between the target class labels. Consequently, in such cases, it is desirable that the relations among the labels can be taken into account in the evaluation measures. For example, if a system assigns an incorrect label to one instance and the assigned label has close relation...

We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary ...