In many real world applications, the example data among different pattern classes are imbalanced and overlapping, which hinder the classification performance of many learning algorithms. In this paper, data cleaning techniques based BNF (the borderline noise factor) is proposed to remove the borderline noise and three under-sampling methods are studied to select the representative majority clas...

Imbalanced data is a challenge for classification models. It reduces the overall performance of traditional learning algorithms. Besides, minority class imbalanced datasets misclassified with high ratio even though this crucial object process. In paper, new model called Lasso-Logistic ensemble proposed to deal by utilizing two popular techniques, random over-sampling and under-sampling. The was...

Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space and a private space. Based on Gaussian Process Latent Variable Models, we pr...

Predicting implantation outcomes of invitro fertilization (IVF) embryos is critical for the success of the treatment. We have applied Naive Bayes classifier to an original IVF dataset in order to discriminate embryos according to implantation potentials. The dataset we analyzed represents an imbalanced distribution of positive and negative instances. In order to deal with the problem of imbalan...

Computational models to predict the developmental toxicity of compounds are built on imbalanced datasets wherein the toxicants outnumber the non-toxicants. Consequently, the results are biased towards the majority class (toxicants). To overcome this problem and to obtain sensitive but also accurate classifiers, we followed an integrated approach wherein (i) Synthetic Minority Over Sampling (SMO...

Several studies have pointed out that class imbalance is a bottleneck in the performance achieved by standard supervised learning systems. However, a complete understanding of how this problem affects the performance of learning is still lacking. In previous work we identified that performance degradation is not solely caused by class imbalances, but is also related to the degree of class overl...

Reliability of a software counts on its fault-prone modules. This means that the less the software consists of fault-prone units, the more we may trust it. Therefore, if we are able to predict the number of fault-prone modules of a software, it will be possible to judge its reliability. In predicting the software fault-prone modules, one of the contributing features is software metric, by which...

When the training data in a two-class classification problem is overwhelmed by one class, most classification techniques fail to correctly identify the data points belonging to the underrepresented class. We propose Similarity-based Imbalanced Classification (SBIC) that learns patterns in the training data based on an empirical similarity function. To take the imbalanced structure of the traini...

Class imbalance data usually suffers from data intrinsic properties beyond that of imbalance alone. The problem is intensified with larger levels of imbalance most commonly found in observational studies. Extreme cases of class imbalance are commonly found in many domains including fraud detection, mammography of cancer and post term births. These rare events are usually the most costly or have...

In the paper we discuss inducing rule-based classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to the remaining classes (majority classes). To improve the ability of a classifier to recognize this class, we propose a new selective pre-processing approach that is applied to data before inducing a rule-based classifier. The approach combines se...