The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is show...

Error-Correcting Output Coding (ECOC) is a general framework for multiclass text classification with a set of binary classifiers. It can not only help a binary classifier solve multi-class classification problems, but also boost the performance of a multi-class classifier. When building each individual binary classifier in ECOC, multiple classes are randomly grouped into two disjoint groups: po...

In many remote sensing projects on land cover mapping, the interest is often in a sub-set of classes presented in the study area. Conventional multi-class classification may lead to a considerable training effort and to the underestimation of the classes of interest. On the other hand, one-class classifiers require much less training, but may overestimate the real extension of the class of inte...

soil salinity has been a large problem in arid and semi arid regions. preparation of such maps is useful for natural resource managers. old methods of preparing such maps require a lot of time and cost. multi-spectral remotely sensed dates due to the broad vision and repeating of these imageries is suitable for provide saline soil maps. this investigation is conducted to provide saline soil map...

Support Vector Machines (SVM) have become a widespread method in machine learning applications. In this paper we studied the one-class SVM method, whose goal is to describe the data from a single class by a set of support vectors. One-class SVMs can be used to construct multiple classifier systems (MCS) utilising the individual one-class data descriptions together with strategies to combine the...

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...

In this article we introduce differential evolution based multiple vector prototype classifier (shortly MVDE). In this method we extend the previous DE classifier so that it can handle several class vectors in one class. Classification problems which are so complex that they are simply not separable by using distance based algorithms e.g. differential evolution (DE) classifier or support vector...

In the last decades many classification methods and fusers have been developed. Considerable gains have been achieved in the classification performance by fusing and combining different classifiers. We experiment a new method for ship infrared imagery recognition based on the fusion of individual results in order to obtain a more reliable decision [1]. To optimize the results of every class of ...