Browsing by Author "Chen, Kuan-Hsi"

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A Two-Stage Learning Method for Response Prediction
(2015)
Chen, Kuan-Hsi
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Ting, Zih-Yun
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Shen, Jia-Ying
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Hu, Yuh-Jyh
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Liang, Tyne
Social networks have become a popular and powerful communication platform as the mobile technology evolves. To evaluate the influence of a message on a social network, response prediction is crucial in modeling the message propagation and interaction among users. To predict whether a new message will receive responses, we propose a two-stage learning method using a new set of features derived from the messages, the users and their responding behaviors. This method first clusters the messages, and then learns the different prediction models from the clusters respectively. The central argument for this two-stage strategy is that the classifiers trained separately from the clustered data sets can focus on particular types of data, reduce the effects of noise, and consequently have an overall higher predictive performance than a single classifier trained from the entire data set. We tested the proposed two-stage learner on Plurk, and compared it with other classifiers. The experimental results show that the two-stage learner outperformed the gradient boosting decision tree learner, the logistic function learner, and the support vector machine for not only the predictive accuracy, but also for the efficiency.
Friendship Prediction on Social Network Users
(2013)
Chen, Kuan-Hsi
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Liang, Tyne
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資訊工程學系
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Department of Computer Science
Undoubtedly friendship is one of key factors which keep social networking service users active and the whole community expanding. Hence, predicting friendships becomes an indispensable service provided by the platforms like Plurk, Twitter and Facebook. In this study, an empirical prediction resolution is presented by taking into account the interactions among Plurk users in Taiwan. Both response links and content information extracted from the interaction corpus are used as features in the implementation of the vector space machine based prediction. Experimental results show that the presented approach outperforms those bag-of-word based methods presented in previous studies.
Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme
(2019-06-10)
Chen, Kuan-Hsi
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Wang, Tsai-Feng
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Hu, Yuh-Jyh
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交大名義發表
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分子醫學與生物工程研究所
BackgroundAlthough various machine learning-based predictors have been developed for estimating protein-protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein-protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features.ResultsWe developed a stacked generalization scheme that integrates five learning algorithms. We also designed three types of protein-pair features based on the physicochemical properties of amino acids, gene ontology annotations, and interaction network topologies. When tested on 19 published datasets collected from eight species, the proposed approach achieved a significantly higher or comparable overall performance, compared with seven competitive predictors.ConclusionWe introduced an ensemble learning approach for PPI prediction that integrated multiple learning algorithms and different protein-pair representations. The extensive comparisons with other state-of-the-art prediction tools demonstrated the feasibility and superiority of the proposed method.