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Full-Text Articles in Engineering
Interpretable Deep Neural Network For Cancer Survival Analysis By Integrating Genomic And Clinical Data, Jie Hao, Youngsoon Kim, Tejaswini Mallavarapu, Jung Hun Oh, Mingon Kang
Interpretable Deep Neural Network For Cancer Survival Analysis By Integrating Genomic And Clinical Data, Jie Hao, Youngsoon Kim, Tejaswini Mallavarapu, Jung Hun Oh, Mingon Kang
Computer Science Faculty Research
Background: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis. Results: We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear …
Two-Stage Bagging Pruning For Reducing The Ensemble Size And Improving The Classification Performance, Hua Zhang, Yujie Song, Bo Jiang, Bi Chen, Guogen Shan
Two-Stage Bagging Pruning For Reducing The Ensemble Size And Improving The Classification Performance, Hua Zhang, Yujie Song, Bo Jiang, Bi Chen, Guogen Shan
Environmental & Occupational Health Faculty Publications
Ensemble methods, such as the traditional bagging algorithm, can usually improve the performance of a single classifier. However, they usually require large storage space as well as relatively time-consuming predictions. Many approaches were developed to reduce the ensemble size and improve the classification performance by pruning the traditional bagging algorithms. In this article, we proposed a two-stage strategy to prune the traditional bagging algorithm by combining two simple approaches: accuracy-based pruning (AP) and distance-based pruning (DP). These two methods, as well as their two combinations, “AP+DP” and “DP+AP” as the two-stage pruning strategy, were all examined. Comparing with the single …
A Graph-Based Reinforcement Learning Method With Converged State Exploration And Exploitation, Han Li, Tianding Chen, Hualiang Teng, Yingtao Jiang
A Graph-Based Reinforcement Learning Method With Converged State Exploration And Exploitation, Han Li, Tianding Chen, Hualiang Teng, Yingtao Jiang
Civil and Environmental Engineering and Construction Faculty Research
In any classical value-based reinforcement learning method, an agent, despite of its continuous interactions with the environment, is yet unable to quickly generate a complete and independent description of the entire environment, leaving the learning method to struggle with a difficult dilemma of choosing between the two tasks, namely exploration and exploitation. This problem becomes more pronounced when the agent has to deal with a dynamic environment, of which the configuration and/or parameters are constantly changing. In this paper, this problem is approached by first mapping a reinforcement learning scheme to a directed graph, and the set that contains all …