We address a one-class classification (OCC) problem aiming at detection of objects that come from a pre-defined target class. Since the non-target class is ill-defined, an effective set of features discriminating between the targets and non-targets is hard to obtain. Alternatively, when raw data are available, dissimilarity representations describing an object by its dissimilarities to a set of...

Prototype classifiers are a type of pattern classifiers, whereby a number of prototypes are designed for each class so as they act as representatives of the patterns of the class. Prototype classifiers are considered among the simplest and best performers in classification problems. However, they need careful positioning of prototypes to capture the distribution of each class region and/or to d...

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others often deleted feature selection process of multi-classifiers, which seriously decreases generalization ability. This paper refers to this phenomenon as interclass interference multi-class problems and analyzes its reason detail. Then, summarizes three...

Error correcting output code (ECOC) is one of the widely used classifier ensemble technique .That technique provide solution for the various multiclass classification problem by dividing multiclass problem into binary class classification problem. In this paper, a new enhanced heuristic coding method, based on ECOC, RACS-ECOC is proposed. To generate strong classifiers for the multiclass classi...

We propose a pre-processing step to classification that applies a clustering algorithm to the training set to discover local patterns in the attribute or input space. We demonstrate how this knowledge can be exploited to enhance the predictive accuracy of simple classifiers. Our focus is mainly on classifiers characterized by high bias but low variance (e.g., linear classifiers); these classifi...

Real-world dynamic systems such as physical and atmosphereocean systems often exhibit a hierarchical system-subsystem structure. However, the paradigm of making this hierarchical/modular structure and the rich properties they encode a “first-class citizen” of machine learning algorithms is largely absent from the literature. Furthermore, traditional data mining approaches focus on designing new...

The paper discusses problems of constructing classifiers from imbalanced data. Re-sampling approaches that change the original class distribution are often used to improve performance of classifiers for the minority class. We describe a new approach to selective pre-processing of imbalanced data which combines local over-sampling of the minority class with filtering difficult examples from majo...

Complex objects are often described by multiple representations modeling various aspects and using various feature transformations. To integrate all information into classification, the common way is to train a classifier on each representation and combine the results based on the local class probabilities. In this paper, we derive so-called confidence estimates for each of the classifiers refl...

In the present work, it is proposed to enhance the learning capabilities and reduce the training time of a competitive learning LAMSTAR neural network using Clustering and Sample Selection algorithm. KDDCUP99 reduced feature data set (Features reduced by PCA algorithm) is used for training and testing the various classifiers. KDDCUP99 dataset has five classes, DOS, PROBE, NORMAL, U2R, and R2L. ...