Kyoung-jae Kim

Learn More
The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers’ needs and expectations is considered a prerequisite for activating the consumer-oriented electronic commerce market. In this study, we propose a novel clustering algorithm based on genetic algorithms (GAs) to effectively segment the online shopping(More)
One of the most important research issues in finance is building effective corporate bankruptcy prediction models because they are essential for the risk management of financial institutions. Researchers have applied various data-driven approaches to enhance prediction performance including statistical and artificial intelligence techniques, and many of(More)
Case-based reasoning (CBR) is one of the most popular prediction techniques in medical domains because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation – its prediction performance is generally lower than other AI techniques like artificial neural networks (ANN). In(More)
Trading in stock market indices has gained unprecedented popularity in major ®nancial markets around the world. However, the prediction of stock price index is a very dif®cult problem because of the complexity of the stock market data. This study proposes stock trading model based on chaotic analysis and piecewise nonlinear model. The core component of the(More)
This paper presents a simultaneous optimization method of a case-based reasoning (CBR) system using a genetic algorithm (GA) for financial forecasting. Prior research proposed many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most research used the GA for improving only a part of architectural(More)
This paper compares a feature transformation method using a genetic algorithm (GA) with two conventional methods for artificial neural networks (ANNs). In this study, the GA is incorporated to improve the learning and generalizability of ANNs for stock market prediction. Daily predictions are conducted and prediction accuracy is measured. In this study,(More)