Yonsei University · Computer Science
Professor Sung-Bae Cho's research lab specializes in intelligent data analysis and machine learning with a strong focus on bioinformatics, biomedical informatics, and cybersecurity. The lab develops advanced computational methods for gene expression analysis, cancer classification, and intrusion detection systems, integrating techniques such as neural networks, fuzzy logic, hidden Markov models, and feature selection algorithms. A central theme across the research is the fusion of soft computing and machine learning to handle uncertainty, noise, and complexity in real-world biological and security data. The lab also contributes to document image analysis, particularly in the structural understanding of technical publications.
Figures are computed from collected data and may differ slightly.
The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. To precisely classify cancer we have to select genes related to cancer because extracted genes from microarray have many noises. In this paper, we attempt to explore many features and classifiers using three benchmark datasets to systematically evaluate the perf
Multiplayer feedforward networks trained by minimizing the mean squared error and by using a one of c teaching function yield network outputs that estimate posterior class probabilities. This provides a sound basis for combining the results from multiple networks to get more accurate classification. This paper presents a method for combining multiple networks based on fuzzy logic, especially the fuzzy integral. This method non-linearly combines objective evidence, in the form of a network output
There are a lot of industrial applications that can be solved competitively by hard computing, while still requiring the tolerance for imprecision and uncertainty that can be exploited by soft computing. This paper presents a novel intrusion detection system (IDS) that models normal behaviors with hidden Markov models (HMM) and attempts to detect intrusions by noting significant deviations from the models. Among several soft computing techniques neural network and fuzzy logic are incorporated in
The explosion of DNA and protein sequence data in public and private databases has been encouraging interdisciplinary research on biology and information technology. Gene expression profiles are just sequences of numbers, and the necessity of tools analyzing them to get useful information has risen significantly. In order to predict the cancer class of patients from the gene expression profile, this paper presents a classification framework that combines a pair of classifiers trained with mutual
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