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Yi-Ping Phoebe Chen
Researcher at La Trobe University
Publications - 287
Citations - 5400
Yi-Ping Phoebe Chen is an academic researcher from La Trobe University. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 33, co-authored 268 publications receiving 4206 citations. Previous affiliations of Yi-Ping Phoebe Chen include Fujian Agriculture and Forestry University & Deakin University.
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Association rule mining to detect factors which contribute to heart disease in males and females
TL;DR: It is seen that factors such as chest pain being asymptomatic and the presence of exercise-induced angina indicate the likely existence of heart disease for both men and women, and resting ECG status is a key distinct factor for heart disease prediction.
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Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
TL;DR: The experimental results demonstrate that the use of MFS noticeably improved the performance, especially in terms of accuracy, for most of the classifiers considered and for majority of the datasets (generated by converting the Cleveland dataset for binary classification).
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Structure-based drug design to augment hit discovery
TL;DR: In silico strategies and modules applied at the level of hit identification and confer the different challenges with possible solutions in enhancing the success rate of the 'hit-to-lead' phase that could eventually help the progress of SBDD in the drug discovery arena are reviewed.
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Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach
TL;DR: A novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy, and an optimized structure of the traffic flow forecasting model with a deep learning approach is presented.
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Acoustic feature selection for automatic emotion recognition from speech
TL;DR: A novel algorithm is presented in this paper, which can be applied on a small sized data set with a high number of features and outperform the commonly used Principle Component Analysis (PCA)/Multi-Dimensional Scaling (MDS) methods, and the more recently developed ISOMap dimensionality reduction method.