An Efficient Fuzzy Classifier Based on Hierarchical Fuzzy Entropy
نویسندگان
چکیده
In an earlier work, Lee et al. [1] presented a simple and fast fuzzy classifier that employed fuzzy entropy to evaluate pattern distribution information in a pattern space. In this paper, we extend his work to propose a new fuzzy classifier based on hierarchical fuzzy entropy (FC-HFE). We retained the main parts of the original structure and modified some methods (e.g., decision of the number of intervals on each dimension and class label assignment). Furthermore, the hierarchical fuzzy entropy is proposed for partitioning the decision region. The proposed FC-HFE can improve the classification accuracy and overcome some of the drawbacks in the Lee et al method. Finally, the FC-HFE is applied to evaluate the classification performance for iris and spiral databases. The simulation results show that the classification rate of the proposed FC-HFE is better than earlier methods.
منابع مشابه
A research on classification performance of fuzzy classifiers based on fuzzy set theory
Due to the complexities of objects and the vagueness of the human mind, it has attracted considerable attention from researchers studying fuzzy classification algorithms. In this paper, we propose a concept of fuzzy relative entropy to measure the divergence between two fuzzy sets. Applying fuzzy relative entropy, we prove the conclusion that patterns with high fuzziness are close to the classi...
متن کاملAn efficient fuzzy classifier with feature selection based on fuzzy entropy
This paper presents an efficient fuzzy classifier with the ability of feature selection based on a fuzzy entropy measure. Fuzzy entropy is employed to evaluate the information of pattern distribution in the pattern space. With this information, we can partition the pattern space into nonoverlapping decision regions for pattern classification. Since the decision regions do not overlap, both the ...
متن کاملEntropy Based Fuzzy Rule Weighting for Hierarchical Intrusion Detection
Predicting different behaviors in computer networks is the subject of many data mining researches. Providing a balanced Intrusion Detection System (IDS) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. Many of the proposed methods perform well in one of the two aspects, and concentrate on a su...
متن کاملSUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS
This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from...
متن کاملGroup Decision Making based on a New Evaluation Method and Hesitant Fuzzy Setting with an Application to an Energy Planning Problem
In recent two decades, countries focused on minimum extraction of fossil fuels and utilized the renewable energies based on countries' policies and the environmental considerations. Thus, choosing the best renewable energy alternative is a significant role to investment on them. Among the classical decision approaches that have used in the literature, the hesitant fuzzy sets (HFSs) theory is ap...
متن کامل