Existing concept learning systems can fail when the negative examples heavily outnumber the positive examples. The paper discusses one essential trouble brought about by imbalanced training sets and presents a learning algorithm addressing this issue. The experiments (with synthetic and real-world data) focus on 2-class problems with examples described with binary and continuous attributes.

Learning in imbalanced domains is one of the recent challenges in machine learning and data mining. In imbalanced classification, data sets present many examples from one class and few from the other class, and the latter class is the one which receives more interest from the point of view of learning. One of the most used techniques to deal with this problem consists in preprocessing the data ...

In this contribution, we study the influence of an Evolutionary Adaptive Inference System with parametric conjunction operators for Fuzzy Rule Based Classification Systems. Specifically, we work in the context of highly imbalanced data-sets, which is a common scenario in real applications, since the number of examples that represents one of the classes of the data-set (usually the concept of in...

Multi-label data classification has become an important and active research topic, where the classification algorithm is required to deal with prediction of sets of label indicators for instances simultaneously. Label powerset (LP) method reduces the multi-label classification problem to a single-label multi-class classification problem by treating each distinct combination of labels. However, ...

We address the analysis of fish trajectories in unconstrained underwater videos to help marine biologist to detect new/rare fish behaviours and to detect environmental changes which can be observed from the abnormal behaviour of fish. The fish trajectories are separated into normal and abnormal classes which indicate the common behaviour of fish and the behaviours that are rare/ unusual respect...

This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...