Muhammad Aminul Islam
Efficient binary fuzzy measure representation and Choquet integral learning
Islam, Muhammad Aminul; Anderson, Derek T.; Du, Xiaoxiao; Havens, Timothy C.; Wagner, Christian
Derek T. Anderson
Timothy C. Havens
CHRISTIAN WAGNER Christian.Wagner@nottingham.ac.uk
Professor of Computer Science
The Choquet integral (ChI), a parametric function for information aggregation, is parameterized by the fuzzy measure (FM), which has 2N real-valued variables for N inputs. However, the ChI incurs huge storage and computational burden due to its exponential complexity relative to N and, as a result, its calculation, storage, and learning becomes intractable for even modest sizes (e.g., N = 15). Inspired by empirical observations in multi-sensor fusion and the more general need to mitigate the storage, computational, and learning limitations, we previously explored the binary ChI (BChI) relative to the binary fuzzy measure (BFM). The BChI is a natural _t for many applications and can be used to approximate others. Previously, we investigated different properties of the BChI and we provided an initial representation. In this article, we propose a new efficient learning algorithm for the BChI, called EBChI, by utilizing the BFM properties that add at most one variable per training instance. Furthermore, we provide an efficient representation of the BFM (EBFM) scheme that further reduces the number of variables required for storage and computation, thus enabling the use of the BChI for \big N". Finally, we conduct experiments on synthetic data that demonstrate the efficiency of our proposed techniques.
Islam, M. A., Anderson, D. T., Du, X., Havens, T. C., & Wagner, C. (2018). Efficient binary fuzzy measure representation and Choquet integral learning. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundationsdoi:10.1007/978-3-319-91473-2_10
|Conference Name||17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2018|
|End Date||Jun 15, 2018|
|Acceptance Date||Feb 3, 2018|
|Publication Date||Jun 11, 2018|
|Deposit Date||Jun 25, 2018|
|Peer Reviewed||Peer Reviewed|
|Book Title||Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf|
|Additional Information||Communications in Computer and Information Science book series, vol. 853|
This file is under embargo due to copyright reasons.
You might also like
Juzzy Constrained: Software for Constrained Interval Type-2 Fuzzy Sets and Systems in Java
Insights from interval-valued ratings of consumer products - a DECSYS appraisal
Performance and Interpretability in Fuzzy Logic Systems – can we have both?
On the Choice of Similarity Measures for Type-2 Fuzzy Sets