Muhammad Aminul Islam

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research-article
Novel Regularization for Learning the Fuzzy Choquet Integral With Limited Training Data
- Siva Krishna Kakula
  Department of Computer Science, Michigan Technological University, Houghton, MI, USA
  ,
- Anthony J. Pinar
  Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA
  ,
- Muhammad Aminul Islam
  Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
  ,
- Derek T. Anderson
  Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
  ,
- Timothy C. Havens
  College of Computing, Michigan Technological University, Houghton, MI, USA
IEEE Transactions on Fuzzy Systems, Volume 29, Issue 10•Oct. 2021, pp 2890-2901 • https://doi.org/10.1109/TFUZZ.2020.3009722
Fuzzy integrals (FIs) are powerful aggregation operators that fuse information from multiple sources. The aggregation is parameterized using a fuzzy measure (FM), which encodes the worths of all subsets of sources. Since the FI is defined with respect to ...
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A Generalized Fuzzy Extension Principle and Its Application to Information Fusion
- Muhammad Aminul Islam
  Department of Electrical and Computer Engineering and Computer Science, University of New Haven, Connecticut, CT, USA
  ,
- Derek T. Anderson
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Timothy C. Havens
  College of Computing, Michigan Technological University, Houghton, MI, USA
  ,
- John E. Ball
  Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
IEEE Transactions on Fuzzy Systems, Volume 29, Issue 9•Sept. 2021, pp 2726-2738 • https://doi.org/10.1109/TFUZZ.2020.3006574
Zadeh's extension principle (ZEP) is a fundamental concept in <italic/>fuzzy set<italic/> (FS) theory that enables crisp mathematical operation on FSs. A well-known shortcoming of ZEP is that the height of the output FS is determined by the lowest ...
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A Weighted Matrix Visualization for Fuzzy Measures and Integrals
- Andrew R. Buck
  University of Missouri (MU),Electrical Engineering and Computer Science (EECS) department,MO,USA
  ,
- Derek T. Anderson
  University of Missouri (MU),Electrical Engineering and Computer Science (EECS) department,MO,USA
  ,
- James M. Keller
  University of Missouri (MU),Electrical Engineering and Computer Science (EECS) department,MO,USA
  ,
- Timothy Wilkin
  School of Information Technology, Deakin University,Victoria,AU
  ,
- Muhammad Aminul Islam
  University of Missouri (MU),Electrical Engineering and Computer Science (EECS) department,MO,USA
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•July 2020, pp 1-8• https://doi.org/10.1109/FUZZ48607.2020.9177775
Fuzzy integrals are useful general purpose aggregation operators, but they can be difficult to understand and visualize in practice. The interaction between an exponentially increasing number of variables–2<sup>n</sup> fuzzy measure variables for n ...
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Possibilistic Clustering Enabled Neuro Fuzzy Logic
- Blake Ruprecht
  University of Missouri (MU),Electrical Engineering and Computer Science (EECS) department,MO
  ,
- Wenlong Wu
  EECS,MU
  ,
- Muhammad Aminul Islam
  EECS,MU
  ,
- Derek Anderson
  EECS,MU
  ,
- James Keller
  EECS,MU
  ,
- Grant Scott
  EECS,MU
  ,
- Curt Davis
  EECS,MU
  ,
- Fred Petry
  U.S. Naval Research Laboratory
  ,
- Paul Elmore
  Johns Hopkins Applied Physics Laboratory
  ,
- Kristen Nock
  U.S. Naval Research Laboratory
  ,
- Elizabeth Gilmour
  U.S. Naval Research Laboratory
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•July 2020, pp 1-8• https://doi.org/10.1109/FUZZ48607.2020.9177593
Artificial neural networks are a dominant force in our modern era of data-driven artificial intelligence. The adaptive neuro fuzzy inference system (ANFIS) is a neural network based on fuzzy logic versus a more traditional premise like convolution. ...
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research-article
Enabling Explainable Fusion in Deep Learning With Fuzzy Integral Neural Networks
- Muhammad Aminul Islam
  Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
  ,
- Derek T. Anderson
  Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
  ,
- Anthony J. Pinar
  Department of Electrical and Computer Engineering and the Department of Computer Science, Michigan Technological University, Houghton, MI, USA
  ,
- Timothy C. Havens
  Department of Electrical and Computer Engineering and the Department of Computer Science, Michigan Technological University, Houghton, MI, USA
  ,
- Grant Scott
  Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
  ,
- James M. Keller
  Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
IEEE Transactions on Fuzzy Systems, Volume 28, Issue 7•July 2020, pp 1291-1300 • https://doi.org/10.1109/TFUZZ.2019.2917124
Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous ...
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research-article
Transfer Learning for the Choquet Integral
- Bryce Murray
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Muhammad Aminul Islam
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Anthony Pinar
  Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA
  ,
- Derek T. Anderson
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Grant Scott
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Timothy C. Havens
  Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA
  ,
- Fred Petry
  Geospatial Sciences and Technology Branch, Naval Research Lab, Stennis Space Center, MS, USA
  ,
- Paul Elmore
  Geospatial Sciences and Technology Branch, Naval Research Lab, Stennis Space Center, MS, USA
2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•June 2019, pp 1-6• https://doi.org/10.1109/FUZZ-IEEE.2019.8858844
The Choquet integral (ChI) is a proven tool for information aggregation. In prior work, we showed that learning a ChI from data results in missing variables. Herein, we explore two ways to transfer a known ChI from a source domain to a new under sampled ...
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Linear Order Statistic Neuron
- Charlie Veal
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Alex Yang
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Alex Hurt
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Muhammad Aminul Islam
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Derek T. Anderson
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Grant Scott
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- James M Keller
  Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, USA
  ,
- Timothy C. Havens
  Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA
  ,
- Bo Tang
  Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA
2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•June 2019, pp 1-6• https://doi.org/10.1109/FUZZ-IEEE.2019.8858802
Herein, a generalization of the ordered weighted average (OWA) is put forth relative to pattern recognition. The resultant linear order statistic neuron (LOSN) is unique in that it bridges fuzzy sets, specifically fuzzy data/information aggregation, with ...
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An efficient evolutionary algorithm to optimize the Choquet integral
- Muhammad Aminul Islam
  Department of Electrical and Computer Engineering Mississippi State University Mississippi State Mississippi
  ,
- Derek T. Anderson
  Department of Electrical Engineering and Computer Science University of Missouri Columbia Missouri
  ,
- Fred Petry
  Geospatial Sciences and Technology Branch, Naval Research Laboratory, Stennis Space Center Hancock County Mississippi
  ,
- Paul Elmore
  Geospatial Sciences and Technology Branch, Naval Research Laboratory, Stennis Space Center Hancock County Mississippi
International Journal of Intelligent Systems, Volume 34, Issue 3•March 2019, pp 366-385 • https://doi.org/10.1002/int.22056
Abstract
Information fusion is an essential part of nearly all systems whose goal is to derive decisions from multiple sources. Often, a fusion solution has parameters and the goal is to learn them from data. Herein, we propose efficient evolutionary ...
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Explainable AI for Understanding Decisions and Data-Driven Optimization of the Choquet Integral
- Bryce Murray
  Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
  ,
- M. Aminul Islam
  Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
  ,
- Anthony J. Pinar
  Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA
  ,
- Timothy C. Havens
  Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, USA
  ,
- Derek T. Anderson
  Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
  ,
- Grant Scott
  Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•July 2018, pp 1-8• https://doi.org/10.1109/FUZZ-IEEE.2018.8491501
To date, numerous ways have been created to learn a fusion solution from data. However, a gap exists in terms of understanding the quality of what was learned and how trustworthy the fusion is for future—i.e., new—data. In part, the current ...
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Visualization and learning of the Choquet integral with limited training data
- Anthony J. Pinar
  Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, Michigan, 49931 USA
  ,
- Timothy C. Havens
  Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, Michigan, 49931 USA
  ,
- Muhammad Aminul Islam
  Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39759, USA
  ,
- Derek T. Anderson
  Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39759, USA
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•July 2017, pp 1-6• https://doi.org/10.1109/FUZZ-IEEE.2017.8015533
The fuzzy integral (FI) is a nonlinear aggregation operator whose behavior is defined by the fuzzy measure (FM). As an aggregation operator, the FI is commonly used for evidence fusion where it combines sources of information based on the worth of each ...
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The fuzzy integral for missing data
- Muhammad Aminul Islam
  Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
  ,
- Derek T. Anderson
  Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA
  ,
- Fred Petry
  Geospatial Sciences and Technology Branch, Naval Research Laboratory, Stennis Space Center, MS, USA
  ,
- Denson Smith
  Geospatial Sciences and Technology Branch, Naval Research Laboratory, Stennis Space Center, MS, USA
  ,
- Paul Elmore
  Geospatial Sciences and Technology Branch, Naval Research Laboratory, Stennis Space Center, MS, USA
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)•July 2017, pp 1-8• https://doi.org/10.1109/FUZZ-IEEE.2017.8015475
Numerous applications in engineering are plagued by incomplete data. The subject explored in this article is how to extend the fuzzy integral (FI), a parametric nonlinear aggregation function, to missing data. We show there is no universally correct ...
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Muhammad Aminul Islam

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Novel Regularization for Learning the Fuzzy Choquet Integral With Limited Training Data

A Generalized Fuzzy Extension Principle and Its Application to Information Fusion

A Weighted Matrix Visualization for Fuzzy Measures and Integrals

Possibilistic Clustering Enabled Neuro Fuzzy Logic

Enabling Explainable Fusion in Deep Learning With Fuzzy Integral Neural Networks

Transfer Learning for the Choquet Integral

Linear Order Statistic Neuron

An efficient evolutionary algorithm to optimize the Choquet integral

Explainable AI for Understanding Decisions and Data-Driven Optimization of the Choquet Integral

Visualization and learning of the Choquet integral with limited training data

The fuzzy integral for missing data