Search Results for author: Hussein A. Abbass

Found 7 papers, 0 papers with code

Does Adversarial Oversampling Help us?

no code implementations20 Aug 2021 Tanmoy Dam, Md Meftahul Ferdaus, Sreenatha G. Anavatti, Senthilnath Jayavelu, Hussein A. Abbass

Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach.

Robust classification

Improving ClusterGAN Using Self-AugmentedInformation Maximization of Disentangling LatentSpaces

no code implementations27 Jul 2021 Tanmoy Dam, Sreenatha G. Anavatti, Hussein A. Abbass

The Latent Space Clustering in Generative adversarial networks (ClusterGAN) method has been successful with high-dimensional data.

Q-Learning with Differential Entropy of Q-Tables

no code implementations26 Jun 2020 Tung D. Nguyen, Kathryn E. Kasmarik, Hussein A. Abbass

It is well-known that information loss can occur in the classic and simple Q-learning algorithm.

Q-Learning

Towards Interpretable Neural Networks: An Exact Transformation to Multi-Class Multivariate Decision Trees

no code implementations10 Mar 2020 Duy T. Nguyen, Kathryn E. Kasmarik, Hussein A. Abbass

While the EC-DT translates the ANN in a layer-wise manner to represent exactly the decision boundaries implicitlylearned by the hidden layers of the network, the Extended C-Net inherits the decompositional approach from EC-DT and combines with a C5 tree learning algorithm to construct the decision rules.

Decision Making

Machine Education: Designing semantically ordered and ontologically guided modular neural networks

no code implementations7 Feb 2020 Hussein A. Abbass, Sondoss Elsawah, Eleni Petraki, Robert Hunjet

The literature on machine teaching, machine education, and curriculum design for machines is in its infancy with sparse papers on the topic primarily focusing on data and model engineering factors to improve machine learning.

Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines

no code implementations27 Feb 2018 Jiangjun Tang, Hussein A. Abbass

The objective of this paper is to test the hypothesis that one can learn from the sequence adopted by the controller some strategies that can act as heuristics in decision support tools for aircraft sequencing.

A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

no code implementations16 Mar 2016 Hussein A. Abbass, George Leu, Kathryn Merrick

Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently.

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