Jongho Lim
Yonsei University · 情報科学
研究室紹介
Professor Jongho Lim's research lab specializes in statistical methodology, data imputation, and machine learning with a focus on developing advanced techniques for handling missing data, improving classification performance in challenging data scenarios, and extracting meaningful insights from unstructured text. The lab emphasizes methodological innovation in multivariate imputation—particularly fractional hot deck imputation (FHDI) and fully efficient fractional imputation (FEFI)—and applies these methods to real-world social and health science data. Additionally, the lab develops practical frameworks for opinion mining and sentiment analysis in user-generated content, and investigates behavioral responses to public health interventions, especially related to social distancing during pandemics. These efforts are grounded in robust statistical inference and variance estimation techniques such as the Jackknife method and replicated weights.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Fractional hot deck imputation (FHDI), proposed by In FHDI, each missing item is filled with multiple observed values yielding a single completed data set for subsequent analyses. An R package FHDI is developed to perform FHDI and also the fully efficient fractional imputation (FEFI) method of FHDI substitutes missing items with a few observed values jointly obtained from a set of donors whereas the FEFI uses all the possible donors. This paper introduces FHDI as a tool for implementing the mult
The performance of a classification model depends significantly on the degree to which the support of each data class overlaps. Successfully distinguishing between classes is difficult if the support is similar. In the one-class classification (OCC) problem, wherein the data comprise only a single class, the classifier performance is significantly degraded if the population support of each class is similar. In this study, we propose a resampling algorithm that enhances classifier performance by
Microplastics present in nature have various toxicities depending on the types or ratio, and these effects can be anticipated through a microplastic toxicity prediction model.
본 연구는 여성가족패널조사(KLoWF) 제3차년도 조사(2012)를 활용하여 미취학자녀를 1명 이상 둔 여성 임금근로자 218명을 연구대상으로 선정하여 양육스트레스에 영향을 미치는 요인을 규명하였다. 연구결과, 가족내 역할인식이 높을수록, 남편 가사노동 분담에 대한 만족도가 높을수록, 양육비용이 적을수록 양육스트레스는 낮은 것으로 나타났다. 이러한 결과를 토대로 함의는, 첫째, 여성 임금근로자 자신이 전통적인 여성의 역할인식을 할 때 오히려 양육스트레스가 높기에, 이제는 주부의 취업에 대한 인식과 맞벌이 부부의 가사업무분담 및 주택의 부부공동명의 등 인식의 전환이 필요하다. 둘째, 남편 가사노동 분담에 따른 만족도가 양육스트레스에 유의미한 영향을 미치는 것으로 나타나, 향후 남편의 가사노동 분담의 효과성에 대한 접근이 필요하다. 후속 연구를 위한 제언은 여성의 직종 중 교대근무를 하는 직종비율이 높음을 감안할 때, 이들의 양육스트레스 등을 연구하기 위한 변수를 추가할 필요가 있다.
A new opinion extraction method is proposed to summarize unstructured, user-generated content (i.e., online customer reviews) in the fixed topic domains. To differentiate the current approach from other opinion extraction approaches, which are often exposed to a sparsity problem and lack of sentiment scores, a confirmatory aspect-based opinion mining framework is introduced along with its practical algorithm called DiSSBUS. In this procedure, 1) each customer review is disintegrated into a set o
Our results demonstrate that public health measures can lead people to practice social distancing. Among them, the measures that strongly encourage voluntary social distancing behaviors would play a critical role in suppressing the infections as it becomes increasingly difficult to continue imposing aggressive restrictions due to practical and economic reasons.