Rough Classifiers
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Hierarchical Rough Classifiers
The major applications of rough set theory in data mining are related to the modeling of concepts using rough classifiers, i.e., the algorithms classifying unseen objects into lower or upper approximations of concepts. This paper investigates a class of compound classifiers called multi-level (or hierarchical) rough classifiers (MLRC). We present the most recent issues on the construction of su...
متن کاملOn Combined Classifiers, Rule Induction and Rough Sets
Problems of using elements of rough sets theory and rule induction to create efficient classifiers are discussed. In the last decade many researches attempted to increase a classification accuracy by combining several classifiers into integrated systems. The main aim of this paper is to summarize the author’s own experience with applying one of his rule induction algorithm, called MODLEM, in th...
متن کاملA Comparison of Three Rough Surface Classifiers
In this paper texture analysis techniques are used to segment rough surfaces into regions of homogeneous texture. The performance of three rough surface classifiers was assessed and compared. The classifiers differ in their discrimination as well as their input and computational requirements. Experiments were used to identify the failure modes of the classifiers and to identify which classifier...
متن کاملClassifiers Based on Two-Layered Learning
In this paper we present an exemplary classifier (classification algorithm) based on two-layered learning. In the first layer of learning a collection of classifiers is induced from a part of original training data set. In the second layer classifiers are induced using patterns extracted from already constructed classifiers on the basis of their performance on the remaining part of training dat...
متن کاملA Novel-weighted Rough Set-based Meta Learning for Ozone Day Prediction
Nowadays, classifier combination methodsreceives great attention from machine learning researchers. It is a powerful tool to improve the accuracy of classifiers. This approach has become increasingly interesting, especially for real-world problems, which are often characterized by their imbalanced nature. The unbalanced distribution of data leads to poor performance of most of the conventional ...
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