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Found 12 papers, 4 papers with code
Applications of Machine Learning in Biopharmaceutical Process Development and Manufacturing: Current Trends, Challenges, and Opportunities
While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biopharmaceuticals, hindering the enormous potential for bioprocesses automation from their development to manufacturing.
hyperbox-brain: A Toolbox for Hyperbox-based Machine Learning Algorithms
hyperbox-brain is an open-source Python library implementing the leading hyperbox-based machine learning algorithms.
The Roles and Modes of Human Interactions with Automated Machine Learning Systems
As automated machine learning (AutoML) systems continue to progress in both sophistication and performance, it becomes important to understand the `how' and `why' of human-computer interaction (HCI) within these frameworks, both current and expected.
Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems
In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed.
An Online Learning Algorithm for a Neuro-Fuzzy Classifier with Mixed-Attribute Data
However, one of the downsides of its original learning algorithms is the inability to handle and learn from the mixed-attribute data.
An in-depth comparison of methods handling mixed-attribute data for general fuzzy min-max neural network
A general fuzzy min-max (GFMM) neural network is one of the efficient neuro-fuzzy systems for classification problems.
Random Hyperboxes
This paper proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set.
Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule
Our proposed approach is based on the mathematical formulas to form a branch-and-bound solution aiming to remove the hyperboxes which are certain not to satisfy expansion or aggregation conditions, and in turn, decreasing the training time of learning algorithms.
An improved online learning algorithm for general fuzzy min-max neural network
This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries.
A comparative study of general fuzzy min-max neural networks for pattern classification problems
General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems.
An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network
Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction.
Hyperbox based machine learning algorithms: A comprehensive survey
With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially.
