Pattern discovery and disentanglement on relational datasets

Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transpar...

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Bibliographic Details
Published in:Scientific reports 2021-03, Vol.11 (1), p.5688-5688, Article 5688
Main Authors: Wong, Andrew K. C., Zhou, Pei-Yuan, Butt, Zahid A.
Format: Article
Language:English
Subjects:
631/114/1305
631/114/2164
692/699
692/700
Cognition
Datasets
Humanities and Social Sciences
Learning algorithms
Machine learning
multidisciplinary
Science
Science (multidisciplinary)
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Summary:Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-84869-4