Senlin Luo

EAAI Journal 2024 Journal Article

FSD-CLCD: Functional semantic distillation graph learning for cross-language code clone detection

• LingHao Zhang
• Senlin Luo
• Limin Pan
• Zhouting Wu
• Kun Gong

AAAI Conference 2020 Conference Paper

Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension

• Xiaorui Zhou
• Senlin Luo
• Yunfang Wu

In reading comprehension, generating sentence-level distractors is a significant task, which requires a deep understanding of the article and question. The traditional entity-centered methods can only generate word-level or phrase-level distractors. Although recently proposed neural-based methods like sequence-to-sequence (Seq2Seq) model show great potential in generating creative text, the previous neural methods for distractor generation ignore two important aspects. First, they didn’t model the interactions between the article and question, making the generated distractors tend to be too general or not relevant to question context. Second, they didn’t emphasize the relationship between the distractor and article, making the generated distractors not semantically relevant to the article and thus fail to form a set of meaningful options. To solve the first problem, we propose a co-attention enhanced hierarchical architecture to better capture the interactions between the article and question, thus guide the decoder to generate more coherent distractors. To alleviate the second problem, we add an additional semantic similarity loss to push the generated distractors more relevant to the article. Experimental results show that our model outperforms several strong baselines on automatic metrics, achieving state-of-the-art performance. Further human evaluation indicates that our generated distractors are more coherent and more educative compared with those distractors generated by baselines.

AIIM Journal 2020 Journal Article

Deep supervised learning with mixture of neural networks

• Yaxian Hu
• Senlin Luo
• Longfei Han
• Limin Pan
• Tiemei Zhang

JBHI Journal 2017 Journal Article

An Intelligible Risk Stratification Model Based on Pairwise and Size Constrained Kmeans

• Longfei Han
• Senlin Luo
• Huaiqing Wang
• Limin Pan
• Xincheng Ma
• Tiemei Zhang

Having a system to stratify individuals according to risk is key to clinical disease prevention. This allows individuals identified at different risk tiers to benefit from further investigation and intervention. But the same risk score estimated for two different persons does not mean they need the same further investigation or represent the similarity health condition between two persons. Meanwhile, users still do not know a prior what most of the risk tiers are, and how many tiers should be found in risk stratification. In this paper, the proposed pairwise and size constrained Kmeans (PSCKmeans) method simultaneously integrates the limited supervised information and the size constraints to screen the high-risk population based on similarity measurement, and gets a feasible and balanced stratification solution to avoid cluster with few points. Results on China Health and Nutrition Survey public dataset and follow-up dataset show that the proposed PSCKmeans method can naturally grade the risk of diabetes into four tiers, and achieve 73. 8%, 85. 1%, and 0. 95% sensitivity, specificity, and ratio of minimum to expected on testing data. The proposed method compares favorably with eight previous semisupervised clustering methods; it demonstrates that semisupervised clustering by unifying multiple forms of constraints can guide a good partition that is more relevant for the domain and find new categories through prior knowledge. Finally, this risk stratification model can provide a tool for risk stratification of clinical disease and be used for further intervention for people with similar health condition.

IJCAI Conference 2017 Conference Paper

Self-paced Mixture of Regressions

• Longfei Han
• Dingwen Zhang
• Dong Huang
• Xiaojun Chang
• Jun Ren
• Senlin Luo
• Junwei Han

Mixture of regressions (MoR) is the well-established and effective approach to model discontinuous and heterogeneous data in regression problems. Existing MoR approaches assume smooth joint distribution for its good anlaytic properties. However, such assumption makes existing MoR very sensitive to intra-component outliers (the noisy training data residing in certain components) and the inter-component imbalance (the different amounts of training data in different components). In this paper, we make the earliest effort on Self-paced Learning (SPL) in MoR, i. e. , Self-paced mixture of regressions (SPMoR) model. We propose a novel self-paced regularizer based on the Exclusive LASSO, which improves inter-component balance of training data. As a robust learning regime, SPL pursues confidence sample reasoning. To demonstrate the effectiveness of SPMoR, we conducted experiments on both the sythetic examples and real-world applications to age estimation and glucose estimation. The results show that SPMoR outperforms the state-of-the-arts methods.

JBHI Journal 2015 Journal Article

Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes

• Longfei Han
• Senlin Luo
• Jianmin Yu
• Limin Pan
• Songjing Chen

Diabetes mellitus is a chronic disease and a worldwide public health challenge. It has been shown that 50–80% proportion of T2DM is undiagnosed. In this paper, support vector machines are utilized to screen diabetes, and an ensemble learning module is added, which turns the “black box” of SVM decisions into comprehensible and transparent rules, and it is also useful for solving imbalance problem. Results on China Health and Nutrition Survey data show that the proposed ensemble learning method generates rule sets with weighted average precision 94. 2% and weighted average recall 93. 9% for all classes. Furthermore, the hybrid system can provide a tool for diagnosis of diabetes, and it supports a second opinion for lay users.