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Pei-Yuan Zhou
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2020 – today
- 2023
- [j6]Andrew K. C. Wong, Pei-Yuan Zhou
, Annie E.-S. Lee:
Theory and rationale of interpretable all-in-one pattern discovery and disentanglement system. npj Digit. Medicine 6 (2023) - [c7]Malikeh Ehghaghi, Pei-Yuan Zhou, Wendy Yusi Cheng, Sahar Rajabi, Chih-Hao Kuo, En-Shiun Annie Lee:
Interpretable Disease Prediction from Clinical Text by Leveraging Pattern Disentanglement. BHI 2023: 1-7 - 2021
- [j5]Pei-Yuan Zhou
Explanation and prediction of clinical data with imbalanced class distribution based on pattern discovery and disentanglement. BMC Medical Informatics Decis. Mak. 21(1): 16 (2021)
2010 – 2019
- 2018
- [j4]Pei-Yuan Zhou
Fuzzy Feature Extraction for Multichannel EEG Classification. IEEE Trans. Cogn. Dev. Syst. 10(2): 267-279 (2018) - [j3]Pei-Yuan Zhou
Corporate Communication Network and Stock Price Movements: Insights From Data Mining. IEEE Trans. Comput. Soc. Syst. 5(2): 391-402 (2018) - 2017
- [c6]Pei-Yuan Zhou
Discovery and disentanglement of protein aligned pattern clusters to reveal subtle functional subgroups. BIBM 2017: 62-69 - 2016
- [j2]Pei-Yuan Zhou
An Effective Pattern Pruning and Summarization Method Retaining High Quality Patterns With High Area Coverage in Relational Datasets. IEEE Access 4: 7847-7858 (2016) - 2015
- [j1]Yang Liu, Yan Liu, Chaoguang Wang
What Strikes the Strings of Your Heart?-Multi-Label Dimensionality Reduction for Music Emotion Analysis via Brain Imaging. IEEE Trans. Auton. Ment. Dev. 7(3): 176-188 (2015) - [c5]Pei-Yuan Zhou
An unsupervised attribute clustering algorithm for unsupervised feature selection. DSAA 2015: 1-7 - [c4]Pei-Yuan Zhou
A Feature Extraction Method for Multivariate Time Series Classification Using Temporal Patterns. PAKDD (2) 2015: 409-421 - 2014
- [c3]Pei-Yuan Zhou
A Model-Based Multivariate Time Series Clustering Algorithm. PAKDD Workshops 2014: 805-817 - [c2]Pei-Yuan Zhou
A two-stage classification framework for imbalanced data with overlapping labels. SOLI 2014: 350-355 - 2013
- [c1]Pei-Yuan Zhou
Regrouping of pattern clusters to reveal characteristics of distinct classes and related classes. BIBM 2013: 55-61
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