This paper proposes an approach to deriving a fuzzy classifier based on evolutionary supervised c... more This paper proposes an approach to deriving a fuzzy classifier based on evolutionary supervised clustering, which identifies the optimal clusters necessary to classify classes. The clusters are formed by multi-dimensional weighted Euclidean distance, which allows clusters of varying shapes and sizes. A cluster induces a Gaussian fuzzy antecedent set with unique variance in each dimension, which reflects the tightness of the cluster. The fuzzy classifier is composed of as many classification rules as classes. The clusters identified for each class constitute fuzzy sets, which are joined by an "and" connective in the antecedent part of the corresponding rule. The approach is evaluated using six data sets. The comparative results with different classifiers are given.
International Journal of Control Automation and Systems, 2010
In this paper, we present a new morphology-based homomorphic filtering technique for feature enha... more In this paper, we present a new morphology-based homomorphic filtering technique for feature enhancement in medical images. The proposed method is based on decomposing an image into morphological subbands. The homomorphic filtering is performed using the morphological subbands. The differential evolution algorithm is applied to find an optimal gain and structuring element for each subband. Simulations show that the proposed filter improves the contrast of the features in medical images.
To classify the significant wavelet coefficients into edge area and noise area, a morphological c... more To classify the significant wavelet coefficients into edge area and noise area, a morphological clustering filter applied to wavelet shrinkage is introduced. New methods for wavelet shrinkage using morphological clustering filter are used in noise removal, and the performance is evaluated under various noise conditions.
International Journal of Control Automation and Systems, 2010
Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy m... more Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy models have recently been used to predict stock market prices because they are capable of extracting useful information from large sets of data without any assumption about a mathematical model. In this paper, three types of fuzzy rule formats to predict daily and weekly stock price indexes were presented. Their premises and consequences were composed of trapezoidal membership functions and novel nonlinear equations, respectively. As the most effective indicators for stock prediction, the information used in traditional candle stick-chart analysis was newly employed as input variables of our fuzzy models. The optimal fuzzy models were identified through an evolutionary process of differential evolution (DE). The different types of fuzzy models to predict the daily and weekly open, high, low, and close prices of the Korea Composite Stock Price Index (KOSPI) were built, and their performances were compared.
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Papers by Heesoo Hwang