Search Results for author:
Found 97 papers, 3 papers with code
Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing
In this paper, we present a systematic review of the prediction with the reject option in the context of various neural networks.
On the tightness of information-theoretic bounds on generalization error of learning algorithms
However, such a learning rate is typically considered to be ``slow", compared to a ``fast rate" of $O(\lambda/n)$ in many learning scenarios.
Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling
Finally, we demonstrate that hybridising the machine learning-based variable ordering methods with traditional domain-based methods is beneficial.
Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data
In biomanufacturing, developing an accurate model to simulate the complex dynamics of bioprocesses is an important yet challenging task.
An Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications
Specifically, we provide generalization error upper bounds for the empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase.
On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis
We show that in causal learning, the excess risk depends on the size of the source sample at a rate of O(1/m) only if the labelling distribution between the source and target domains remains unchanged.
Fast Rate Generalization Error Bounds: Variations on a Theme
However, such a learning rate is typically considered to be "slow", compared to a "fast rate" of O(1/n) in many learning scenarios.
Multi-objective Semi-supervised Clustering for Finding Predictive Clusters
This study concentrates on clustering problems and aims to find compact clusters that are informative regarding the outcome variable.
A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms
Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem.
Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge
On the one hand, it is conceivable that knowledge from one task could be useful for solving a related problem.
Handling uncertainty using features from pathology: opportunities in primary care data for developing high risk cancer survival methods
As past medical data about a patient can be incomplete, irregular or missing, this creates additional challenges when attempting to use the patient's history for any new diagnosis.
A new interval-based aggregation approach based on bagging and Interval Agreement Approach (IAA) in ensemble learning
In this paper, in addition to implementing a new aggregation approach in ensemble learning, we designed some experiments to encourage researchers to use interval modeling in ensemble learning because it preserves more uncertainty and this leads to more accurate classification.
On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models
This has resulted in the creation of numerous risk stratification tools with the objective of formulating associated surgical risk to assist both surgeons and patients in decision-making.
Data-Driven Regular Expressions Evolution for Medical Text Classification Using Genetic Programming
This study proposes a novel regular expression-based text classification method making use of genetic programming (GP) approaches to evolve regular expressions that can classify a given medical text inquiry with satisfactory precision and recall while allow human to read the classifier and fine-tune accordingly if necessary.
Similarity measure for aggregated fuzzy numbers from interval-valued data
The similarity measure proposed within this study contains several features and attributes, of which are novel to aggregated fuzzy numbers.
Machine learning with incomplete datasets using multi-objective optimization models
We investigate the reliability and robustness of the proposed model using experiments by defining several scenarios in dealing with missing values and classification.
Methods of ranking for aggregated fuzzy numbers from interval-valued data
The two proposed ranking methods within this study contain the combination and application of previously proposed similarity measures, along with attributes novel to that of aggregated fuzzy numbers from interval-valued data.
A Hybrid Pricing and Cutting Approach for the Multi-Shift Full Truckload Vehicle Routing Problem
This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm).
Multicriteria Group Decision-Making Under Uncertainty Using Interval Data and Cloud Models
The proposed MCGDM algorithm aggregates the data, determines the optimal weights for criteria and ranks alternatives with no further input.
Transfer learning to enhance amenorrhea status prediction in cancer and fertility data with missing values
Also, missing values are unavoidable in health and medical datasets and tackling the problem arising from the inadequate instances and missingness is not straightforward (Snell, et al. 2017, Sterne, et al. 2009).
Teaching Key Machine Learning Principles Using Anti-learning Datasets
Much of the teaching of machine learning focuses on iterative hill-climbing approaches and the use of local knowledge to gain information leading to local or global maxima.
Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing and Word-vector Models
In this paper, we propose a rule-based engine composed of high quality and interpretable regular expressions for medical text classification.
Measuring agreement on linguistic expressions in medical treatment scenarios
The measure has been specifically designed for assessing this agreement in fuzzy sets which are generated from data such as patient responses.
Imputation techniques on missing values in breast cancer treatment and fertility data
Clinical decision support using data mining techniques offers more intelligent way to reduce the decision error in the last few years.
Fuzzy C-means-based scenario bundling for stochastic service network design
Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP).
Using simulation to incorporate dynamic criteria into multiple criteria decision-making
In this paper, we present a case study demonstrating how dynamic and uncertain criteria can be incorporated into a multicriteria analysis with the help of discrete event simulation.
Multi-objective semi-supervised clustering to identify health service patterns for injured patients
The practical purpose of developing this pattern recognition method is to group patients, who are injured in transport accidents, in the early stages post-injury.
Uncertainty measures for probabilistic hesitant fuzzy sets in multiple criteria decision making
This contribution reviews critically the existing entropy measures for probabilistic hesitant fuzzy sets (PHFSs), and demonstrates that these entropy measures fail to effectively distinguish a variety of different PHFSs in some cases.
Retrieving and ranking short medical questions with two stages neural matching model
Internet hospital is a rising business thanks to recent advances in mobile web technology and high demand of health care services.
Higher order hesitant fuzzy Choquet integral operator and its application to multiple criteria decision making
This verifies that we have to implement fuzzy measures for modelling the interaction phenomena among the criteria. On the other hand, based on the recent extension of hesitant fuzzy set, called higher order hesitant fuzzy set (HOHFS) which allows the membership of a given element to be defined in forms of several possible generalized types of fuzzy set, we encourage to propose the higher order hesitant fuzzy (HOHF) Choquet integral operator.
A Synthetic Over-sampling method with Minority and Majority classes for imbalance problems
Moreover, existing methods that generate synthetic instances using the majority class distributional information cannot perform effectively when the majority class has a multi-modal distribution.
Information-theoretic analysis for transfer learning
Specifically, we provide generalization error upper bounds for general transfer learning algorithms and extend the results to a specific empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase.
CRNN: A Joint Neural Network for Redundancy Detection
We observe that MGU achieves the optimal runtime and comparable performance against GRU and LSTM.
Measuring Player's Behaviour Change over Time in Public Goods Game
External clustering indices were originally used to measure the difference between suggested clusters in terms of clustering algorithms and ground truth labels for items provided by experts.
Modelling Cyber-Security Experts' Decision Making Processes using Aggregation Operators
An important role carried out by cyber-security experts is the assessment of proposed computer systems, during their design stage.
Self-Organising Maps in Computer Security
The field of computer security tends to accept the latter view as a more appropriate approach due to its more workable validation and verification possibilities.
Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations
Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality.
An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates
The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours.
Exploring Differences in Interpretation of Words Essential in Medical Expert-Patient Communication
As the results of these questionnaires are often used to assess patient progress and to determine future treatment options, it is important to establish that the words used are interpreted in the same way by both patients and medical professionals.
Applying Interval Type-2 Fuzzy Rule Based Classifiers Through a Cluster-Based Class Representation
Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i. e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules.
Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams
Uncertain data streams have been widely generated in many Web applications.
Simulating user learning in authoritative technology adoption: An agent based model for council-led smart meter deployment planning in the UK
In this paper we investigate user learning in authoritative technology adoption by developing an agent-based model using the case of council-led smart meter deployment in the UK City of Leeds.
Identifying Candidate Risk Factors for Prescription Drug Side Effects using Causal Contrast Set Mining
Big longitudinal observational databases present the opportunity to extract new knowledge in a cost effective manner.
Optimising Rule-Based Classification in Temporal Data
Based on these initial classifications, the optimisation process tries to find an improved classifier which produces the best possible compact classes of objects (players) for every time point in the temporal data.
Indebted households profiling: a knowledge discovery from database approach
In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencies and associations amongst the available continuous and categorical variables altogether and accurately generate profiles of most interesting household segments according to their credit risk.
Modelling Office Energy Consumption: An Agent Based Approach
In this paper, we develop an agent-based model which integrates four important elements, i. e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, based on a case study, to simulate the energy consumption in office buildings.
Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining
We then implemented a cohort study design using regularised cox regression that incorporated and accounted for the candidate confounding interaction terms.
Juxtaposition of System Dynamics and Agent-based Simulation for a Case Study in Immunosenescence
Immunosenescence, the ageing of the immune system, is highly correlated to the negative effects of ageing, such as the increase of auto-inflammatory diseases and decrease in responsiveness to new diseases.
An Improved System for Sentence-level Novelty Detection in Textual Streams
Novelty detection in news events has long been a difficult problem.
Personalising Mobile Advertising Based on Users Installed Apps
The results showed there were clear differences in the way the profiles interact with the different advert genres and the results of this paper suggest that mobile advert targeting would improve the frequency that users interact with an advert.
A Data Mining framework to model Consumer Indebtedness with Psychological Factors
Modelling Consumer Indebtedness has proven to be a problem of complex nature.
Refining Adverse Drug Reactions using Association Rule Mining for Electronic Healthcare Data
Side effects of prescribed medications are a common occurrence.
Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance
This is especially true where a large number of fuzzy sets are being compared as part of a reasoning system.
Augmented Neural Networks for Modelling Consumer Indebtness
Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models.
Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms
The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier.
Attributes for Causal Inference in Longitudinal Observational Databases
In this paper we investigate potential attributes that can be used in causal inference to identify side effects based on the Bradford-Hill causality criteria.
A Fuzzy Directional Distance Measure
The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory, however, distance measures currently within the literature use a crisp value to represent the distance between fuzzy sets.
Variability of Behaviour in Electricity Load Profile Clustering; Who Does Things at the Same Time Each Day?
Whether using the variability of regular behaviour allows the creation of more consistent groupings of households is investigated and compared with daily load profile clustering.
Ensemble Learning of Colorectal Cancer Survival Rates
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours.
A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery
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 undesirable side effects.
Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database
Then feature selection methods are performed on feature matrix to detect the significant features.
Comparison of algorithms that detect drug side effects using electronic healthcare databases
In this paper we apply four existing electronic healthcare database signal detecting algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the THIN database for a selection of drugs from six chosen drug families.
Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.
Data classification using the Dempster-Shafer method
In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems.
Measuring the Directional Distance Between Fuzzy Sets
The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory.
Extending Similarity Measures of Interval Type-2 Fuzzy Sets to General Type-2 Fuzzy Sets
Similarity measures provide one of the core tools that enable reasoning about fuzzy sets.
Performance Measurement Under Increasing Environmental Uncertainty In The Context of Interval Type-2 Fuzzy Logic Based Robotic Sailing
Performance measurement of robotic controllers based on fuzzy logic, operating under uncertainty, is a subject area which has been somewhat ignored in the current literature.
Artificial Immune Systems (INTROS 2)
The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time.
Using Clustering to extract Personality Information from socio economic data
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 principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding complicated economic behaviours.
Finding the creatures of habit; Clustering households based on their flexibility in using electricity
Changes in the UK electricity market, particularly with the roll out of smart meters, will provide greatly increased opportunities for initiatives intended to change households' electricity usage patterns for the benefit of the overall system.
Supervised Learning and Anti-learning of Colorectal Cancer Classes and Survival Rates from Cellular Biology Parameters
For predicting the stage of cancer from the immunological attributes, anti-learning approaches outperform a range of popular algorithms.
Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours.
Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases
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 detecting negative side effects.
Creating Personalised Energy Plans. From Groups to Individuals using Fuzzy C Means Clustering
Changes in the UK electricity market mean that domestic users will be required to modify their usage behaviour in order that supplies can be maintained.
The Application of a Data Mining Framework to Energy Usage Profiling in Domestic Residences using UK data
This paper describes a method for defining representative load profiles for domestic electricity users in the UK.
Discovering Sequential Patterns in a UK General Practice Database
The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses.
Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm
It is found that this feature, while advantageous for noisy, time-ordered classification, is not as useful as a traditional static filter for processing a synthetic dataset.
Detect adverse drug reactions for drug Alendronate
Adverse drug reaction (ADR) is widely concerned for public health issue.
Examining the Classification Accuracy of TSVMs with ?Feature Selection in Comparison with the GLAD Algorithm
Therefore, in this paper, we suggest Transductive Support Vector Machines (TSVMs) as semi-supervised learning algorithms, learning with both labelled samples data and unlabelled samples to perform the classification of microarray data.
An investigation into the relationship between type-2 FOU size and environmental uncertainty in robotic control
It has been suggested that, when faced with large amounts of uncertainty in situations of automated control, type-2 fuzzy logic based controllers will out-perform the simpler type-1 varieties due to the latter lacking the flexibility to adapt accordingly.
A Comparison of Non-stationary, Type-2 and Dual Surface Fuzzy Control
Type-1 fuzzy logic has frequently been used in control systems.
Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database
In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared.
Application of a clustering framework to UK domestic electricity data
This paper takes an approach to clustering domestic electricity load profiles that has been successfully used with data from Portugal and applies it to UK data.
Theoretical formulation and analysis of the deterministic dendritic cell algorithm
Our analysis suggests that the standard dDCA has a runtime complexity of O(n2) for the worst-case scenario, where n is the number of input data instances.
Modelling Electricity Consumption in Office Buildings: An Agent Based Approach
In this paper, we develop an agent-based model which integrates four important elements, i. e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, to simulate the electricity consumption in office buildings.
Motif Detection Inspired by Immune Memory (JORS)
In this paper we test the flexibility of the motif tracking algorithm by applying it to the search for patterns in two industrial data sets.
Privileged Information for Data Clustering
This method has the ability to utilize a wide variety of clustering techniques, individually or in combination, while fusing privileged and technical data for improved clustering.
Investigating Mathematical Models of Immuno-Interactions with Early-Stage Cancer under an Agent-Based Modelling Perspective
Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations.
Real-world Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms
In mobile robotics, a solid test for adaptation is the ability of a control system to function not only in a diverse number of physical environments, but also on a number of different robotic platforms.
Validation of a Microsimulation of the Port of Dover
Modelling and simulating the traffic of heavily used but secure environments such as seaports and airports is of increasing importance.
The Dendritic Cell Algorithm for Intrusion Detection
Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection.
Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data
How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis.
Immune System Approaches to Intrusion Detection - A Review (ICARIS)
The use of artificial immune systems in intrusion detection is an appealing concept for two reasons.
Memory Implementations - Current Alternatives
Memory can be defined as the ability to retain and recall information in a diverse range of forms.
Modelling and Analysing Cargo Screening Processes: A Project Outline
The efficiency of current cargo screening processes at sea and air ports is unknown as no benchmarks exists against which they could be measured.
Dienstplanerstellung in Krankenhaeusern mittels genetischer Algorithmen
This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.
Building Better Nurse Scheduling Algorithms
The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms.
