15 citations · 16 across the 5 of their papers we have counts for
5 papers
Bias in Filter Feature Selection Evaluation: A Meta-Analysis of Datasets, Baselines, and Experimental Design Choices
Malick Ebiele, Malika Bendechache, Rob Brennan
Background: Since 1990 many feature selection methods have been proposed across heterogeneous applications. To validate the usefulness of a new method, it needs to be compared agai…
KGroups: A Versatile Univariate Max-Relevance Min-Redundancy Feature Selection Algorithm for High-dimensional Biological Data
Malick Ebiele, Malika Bendechache, Rob Brennan
This paper proposes a new univariate filter feature selection (FFS) algorithm called KGroups. The majority of work in the literature focuses on investigating the relevance or redun…
Personalization of Dataset Retrieval Results using a Metadata-based Data Valuation Method
Malick Ebiele, Malika Bendechache, Eamonn Clinton +1
In this paper, we propose a novel data valuation method for a Dataset Retrieval (DR) use case in Ireland's National mapping agency. To the best of our knowledge, data valuation has…
Image Data Augmentation Approaches: A Comprehensive Survey and Future directions
Teerath Kumar, Alessandra Mileo, Rob Brennan +1
Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, wher…
Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets
Sidra Aleem, Teerath Kumar, Suzanne Little +3
Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learn…