Machine learning approach has got major importance when distribution of data is unknown. Classification of data from the data set causes some problem when distribution of data is unknown. Characterization of raw data relates to whether the data can take on only discrete values or whether the data is continuous. In real world application data drawn from non-stationary distribution, causes the pr...

Imbalanced data, where the number of instances of one class is much higher than the others, are frequent in many domains such as fraud detection, telecommunications management, oil spill detection and text classification. Traditional classifiers do not perform well when considering data that are susceptible to both within-class and between-class imbalances. In this paper, we propose the ClustFi...

Detecting and adapting to concept drift makes learning data stream classifiers a difficult task. It becomes even more complex when the distribution of classes in the stream becomes imbalanced. Currently, proper assessment of classifiers for such data is still a challenge, as existing evaluation measures either do not take into account class imbalance or are unable to indicate class ratio change...

Determining whether an imprecise dataset is imbalanced is not immediate. The vagueness in the data causes that the prior probabilities of the classes are not precisely known, and therefore the degree of imbalance can also be uncertain. In this paper we propose suitable extensions of different resampling algorithms that can be applied to interval valued, multi-labelled data. By means of these ex...

We propose an algorithm for two-class classification problems when the training data are imbalanced. This means the number of training instances in one of the classes is so low that the conventional classification algorithms become ineffective in detecting the minority class. We present a modification of the kernel Fisher discriminant analysis such that the imbalanced nature of the problem is e...

In binary classification problems it is common for the two classes to be imbalanced: one case is very rare compared to the other. In this paper we consider the infinitely imbalanced case where one class has a finite sample size and the other class’s sample size grows without bound. For logistic regression, the infinitely imbalanced case often has a useful solution. Under mild conditions, the in...

In classification tasks with imbalanced datasets the distribution of examples between the classes is uneven. However, it is not the imbalance itself which hinders the performance, but there are other related intrinsic data characteristics which have a significance in the final accuracy. Among all, the overlapping between the classes is possibly the most significant one for a correct discriminat...

In this paper we investigate the suitability of a fuzzy system as a classifier for imbalanced data problems. Primarily, the fuzzy model performance is evaluated on artificial data sets, generated with various levels of size, complexity and imbalance. It is investigated what combination of the three problematic issues makes the learning problem harder [4]. A theoretic analysis shows that for a f...

In this work, we propose two stochastic architectural models (CMC and CMC-M ) with two layers of classifiers applicable to datasets with one and multiple skewed classes. This distinction becomes important when the datasets have a large number of classes. Therefore, we present a novel solution to imbalanced multiclass learning with several skewed majority classes, which improves minority classes...

We studied three methods to improve identification of difficult small classes by balancing imbalanced class distribution with data reduction. The new method, neighborhood cleaning rule (NCL), outperformed simple random and one-sided selection methods in experiments with ten data sets. All reduction methods improved identification of small classes (20-30%), but the differences were insignificant...