85 citations · 220 across the 29 of their papers we have counts for
13 papers · 1 filter
Clustering-Based Representation Learning through Output Translation and Its Application to Remote--Sensing Images
Qinglin Li, Bin Li, Jonathan M Garibaldi +1
In supervised deep learning, learning good representations for remote--sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obta…
A Novel Meta Learning Framework for Feature Selection using Data Synthesis and Fuzzy Similarity
Zixiao Shen, Xin Chen, Jonathan M. Garibaldi
This paper presents a novel meta learning framework for feature selection (FS) based on fuzzy similarity. The proposed method aims to recommend the best FS method from four candida…
A Novel Weighted Combination Method for Feature Selection using Fuzzy Sets
Zixiao Shen, Xin Chen, Jonathan M. Garibaldi
In this paper, we propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fu…
Performance Optimization of a Fuzzy Entropy based Feature Selection and Classification Framework
Zixiao Shen, Xin Chen, Jonathan M. Garibaldi
In this paper, based on a fuzzy entropy feature selection framework, different methods have been implemented and compared to improve the key components of the framework. Those meth…
A Data Mining framework to model Consumer Indebtedness with Psychological Factors
Alexandros Ladas, Eamonn Ferguson, Uwe Aickelin +1
Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consu…
Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms
Jenna M. Reps, Uwe Aickelin, Jonathan M. Garibaldi
In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side…
Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Jenna M. Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
Background: Children are frequently prescribed medication off-label, meaning there has not been sufficient testing of the medication to determine its safety or effectiveness. The m…
A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of u…
Comparison of algorithms that detect drug side effects using electronic healthcare databases
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Heal…
Using Clustering to extract Personality Information from socio economic data
Alexandros Ladas, Uwe Aickelin, Jon Garibaldi +1
It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration princip…
Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detect…
Discovering Sequential Patterns in a UK General Practice Database
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be…
Examining the Classification Accuracy of TSVMs with ?Feature Selection in Comparison with the GLAD Algorithm
Hala Helmi, Jon M. Garibaldi, Uwe Aickelin
Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled exa…