Since the performance of a character recognition system is mainly determined by the classifier, we introduce one that is especially tailored to our application. Working with 100 different classes, the most important properties of a reliable classifier are a high generalization capability, robustness to noise and classification speed. For this reason, we designed a classifier that is a combinati...

Forest types mapping, is one of the most necessary elements in the forest management and silviculture treatments. Traditional methods such as field surveys are almost time-consuming and cost-intensive. Improvements in remote sensing data sources and classification –estimation methods are preparing new opportunities for obtaining more accurate forest biophysical attributes maps. This research co...

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

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...

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...

Target identification from high resolution remote sensing image is a common task for many applications. In order to improve the performance of target identification, multiple classifier combination is used to QuickBird high resolution image, and some key techniques including selection and design of member classifiers, classifier combination algorithm and target identification methods are invest...

In this paper, we introduce a new restricted Bayesian network classifier that extends naive Bayes by relaxing the conditional independence assumptions, and show that it is partly generative and partly discriminative. Experimental results show that the hybrid classifier performs better than a purely generative classifier (naive Bayes) or a purely discriminative classifier (Logistic Regression) a...