Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting base classifiers for each detected drift. This approach prevents underlying becoming outdated as distribution a data stream shifts from one concept another. In situations where both and temporal dependence are present within stream, forced can cause complications in classifier eva...

Data classification plays important role in the field of data mining. The increasing rate of data diversity and size decrease the performance and efficiency of classifier. The decreasing performance of classifier compromised with unvoted data of classifier. Now the merging of two or more classifier for better prediction and voting of data are used, such techniques are called Ensemble classifier...

In this paper, we evaluate and contrast two fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimisation and boosting whereas the second classifier is based on a fuzzy neural network. We show that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5 algorithm that we included as a ref...

Combining the computational fluid dynamics (CFD) and the design of experiments (DOE) methods, as a mixed approach in modeling was proposed so that to simultaneously benefit from the advantages of both modeling methods. The presented method was validated using a coal hydraulic classifier in an industrial scale. Effects of operating parameters including feed flow rate, solid content and baffle le...

Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other norma...

Recent findings in the domain of combining classifiers provide a surprising revision of the usefulness of diversity for modelling combined performance. Although there is a common agreement that a successful fusion system should be composed of accurate and diverse classifiers, experimental results show very weak correlations between various diversity measures and combining methods. Effectively n...