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...

We present in this paper a simple, yet valuable improvement to the traditional k-Nearest Neighbor (kNN) classifier. It aims at addressing the issue of unbalanced classes by maximizing the class-wise classification accuracy. The proposed classifier also gives the option of favoring a particular class through evaluating a small set of fuzzy rules. When tested on a number of UCI datasets, the prop...

This paper studies the effects of variable selection and class distribution on the performance of specific logit regression (i.e., a primitive classifier system) and artificial neural network (ANN; a relatively more sophisticated classifier system) implementations in a customer relationship management (CRM) setting. Finally, ensemble models are constructed by combining the predictions of multip...

The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experime...

Density models are a popular tool for building classifiers. When using density models to build a classifier, one typically learns a separate density model for each class of interest. These density models are then combined to make a classifier through the use of Bayes’ rule utilizing the prior distribution over the classes. In this paper, we provide a discriminative method for choosing among alt...

In this project a classifier is developed that can classify a blog into different categories based on the topics that author frequently writes about. Treating each blog as a document and each topic category as a class, a SVM based multi-class classifier is developed. The impact of feature selection has been studied by using different methods to generate the feature vector from the documents.

Reliable classifiers abstain from uncertain instance classifications. In this paper we extend our previous approach to construct reliable classifiers which is based on isometrics in Receiver Operator Characteristic (ROC) space. We analyze the conditions to obtain a reliable classifier with higher performance than previously possible. Our results show that the approach is generally applicable to...

We study consistency of learning algorithms for a multi-class performance metric that is anon-decomposable function of the confusion matrix of a classifier and cannot be expressed asa sum of losses on individual data points; examples of such performance metrics include themicro and macro F-measure used widely in information retrieval and the multi-class G-meanmetric popular in c...

Contrary to binary and multi-class classifiers, the purpose of a one-class classifier for remote sensing applications is to map only one specific land use/land cover class of interest. Training these classifiers exclusively requires reference data for the class of interest, while training data for other classes is not required. Thus, the acquisition of reference data can be significantly reduce...

We consider the problem of learning a classifier using only positive and unlabeled samples. In this setting, it is known that a classifier can be successfully learned if the class prior is available. However, in practice, the class prior is unknown and thus must be estimated from data. In this paper, we propose a new method to estimate the class prior by partially matching the class-conditional...