An approach to construct a new classifier called an intuitionistic fuzzy decision tree is presented. Well known benchmark data is used to analyze the performance of the classifier. The results are compared to some other popular classification algorithms. Finally, the classifier behavior is verified while solving a real-world classification problem.

The paper presents a hybrid fuzzy classifier for effective land use land cover mapping. It discusses a Bayesian way of incorporating spatial contextual information into the Fuzzy Noise Classifier (FNC). The FNC was chosen, as it detects noise using spectral information more efficiently than its fuzzy counterparts. The spatial information at the level of second order pixel neighbourhood was mode...

In this paper, a hybrid classifier using fuzzy clustering and several neural networks has been proposed. With using the fuzzy C-means algorithm, training samples will be clustered and the inappropriate data will be detected and moved to another dataset (RemovedDataset) and used differently in the classification phase. Also, in the proposed method using the membership degree of samples to the cl...

In the recent years, pixel-wise classification of hyperspectral images aroused many developments, and the literature now provides various classifiers for numerous applications. In this chapter, we present a generic framework where the redundant or complementary results provided by multiple classifiers can actually be aggregated. Taking advantage from the specificities of each classifier, the de...

The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural...

This research presents a system for post processing of data that takes mined flat rules as input and discovers crisp as well as fuzzy hierarchical structures using Learning Classifier System approach. Learning Classifier System (LCS) is basically a machine learning technique that combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produ...

It is found that sub-pixel classifiers for classification of multi-spectral remote sensing data yield a higher accuracy. With this objective, a study has been carried out, where fuzzy set theory based sub-pixel classifiers have been compared with statistical based sub-pixel classifier for classification of multi-spectral remote sensing data.Although, a number of Fuzzy set theory based classifie...

Classifier combination is now an established pattern recognition subdiscipline. Despite the strong aspiration for theoretical studies, classifier combination relies mainly on heuristic and empirical solutions. Assuming that “soft computing” encompasses neural networks, evolutionary computation, and fuzzy sets, we explain how each of the three components has been used in classifier combination.

This paper presents a framework for genetic fuzzy rule based classifier. First, a classification problem is divided into several two-class problems following a fuzzy class binarization scheme; next, a fuzzy rule is evolved for each two-class problem using a Michigan iterative learning approach; finally, the evolved fuzzy rules are integrated using the fuzzy class binarization scheme. In particu...

In this paper, we propose a new feature selection method called class dependency based feature selection for dimensionality reduction of the macular disease dataset from pattern electroretinography (PERG) signals. In order to diagnosis of macular disease, we have used class dependency based feature selection as feature selection process, fuzzy weighted pre-processing as weighted process and dec...