Special Issue "Big Data Analytic: From Accuracy to Interpretability"
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (15 February 2018).
Interests: artificial intelligence; deep learning; classification; rule extraction; big data analytics; interpretability of deep neural network
Special Issues and Collections in MDPI journals
The primary disadvantage of Big Data analytics using high-performance classifiers and Deep Learning is that they have no clear declarative representation of knowledge. In addition, the current Big Data analytics have considerable difficulties in generating the necessary explanation structures, which limits their full potential because the ability to provide detailed characterizations of classification strategies would promote their acceptance. Expert systems benefit from a clear declarative representation of knowledge about the problem domain; therefore, a natural means to elucidate the knowledge embedded within neural networks (NNs), support vector machines (SVMs), evolutionary computation (EC) and their hybrids are to extract symbolic rules. However, surprisingly, very little work has been conducted in relation to Big Data analytics. Bridging this gap could be expected to contribute to the real-world utility of Big Data analytics.
Rule extraction from NNs, SVMs, EC and their hybrids can also be considered an optimization problem because it involves a clear trade-off between accuracy and interpretability; although higher number of rules typically provides better accuracy, it also reduces interpretability. Rule extraction from ANNs, SVMs, EC, and their hybrids, therefore, remain an area in need of further innovation.
Potential topics include, but are not limited to, the following:
- Big Data analytics using machine learning and computational intelligence
- Machine learning and computational intelligence applied to transparency of deep learning networks
- Big Data analytics for medical, financial, and industrial big data
- Rule extraction from decision tree ensembles and forests
- Accuracy-interpretability dilemma: high performance classifiers versus rule extraction
Prof. Dr. Yoichi Hayashi
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