Sung‐Bae Cho
연세대학교 Department of Computer Science and Engineering · 컴퓨터과학
Sung-Bae Cho 교수의 연구실은 생물정보학, 의료정보학 및 지능형 데이터 분석을 중심으로, 유전자 발현 데이터 분석, 암 진단 지원 시스템, 침입 탐지 시스템 등에 응용되는 지능형 소프트웨어 기반 기술을 개발하고 있습니다. 특히, 마이크로어레이 데이터에서 유의미한 유전자를 선별하고, 다수의 분류기의 예측 결과를 비선형적으로 융합하는 기법을 통해 정확도를 향상시키는 데 초점을 맞추고 있습니다. 연구는 하이브리드 기반의 지능형 시스템 설계와, 신경망, 퍼지 논리, 은닉 마르코프 모델 등 소프트 컴퓨팅 기법을 융합하여 불확실성과 노이즈에 강건한 분석 기법을 개발하는 데 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
