Papers by Harris Papadopoulos
Reliable Probabilistic Prediction for Medical Decision Support
A Cross-Conformal Predictor for Multi-label Classification
IFIP Advances in Information and Communication Technology, 2014
Reliable Probabilistic Prediction for Medical Decision Support
IFIP Advances in Information and Communication Technology, 2011
[IFIP Advances in Information and Communication Technology] Artificial Intelligence Applications and Innovations Volume 382 || Deployment of pHealth Services upon Always Best Connected Next Generation Network
Special Issue on Selected Papers from the 6th IFIP International Conference on Artificial Intelligence Applications & Innovations (AIAI 2010)
Inductive Conformal Prediction: Theory and Application to Neural Networks
Tools in Artificial Intelligence, 2008
Discussion on Hedging Predictions in Machine Learning by A. Gammerman and V. Vovk
The Computer Journal, 2006
Reliable probabilistic classification with neural networks
Neurocomputing, 2013
Engineering Applications of Neural Networks: 12th International Conference, Eann 2011 and 7th Ifip Wg 12.5 International Conference, Aiai 2011, Corfu, …
... Indicators ..... 491 Lazaros Iliadis and Kyriaki Kitikidou Page 17. Table of Contents Part ... more ... Indicators ..... 491 Lazaros Iliadis and Kyriaki Kitikidou Page 17. Table of Contents Part I XVII An Artificial Intelligence-Based Environment Quality Analysis System ..... 499 ...
Normalized nonconformity measures for regression conformal prediction
... used for regression so far. Unlike traditional regression methods which produce pointpredicti... more ... used for regression so far. Unlike traditional regression methods which produce pointpredictions, Conformal Predictors output predictive regions that satisfy a given confidence level. When the regular regression nonconformity ...
Qualified Prediction for Large Data Sets in the Case of Pattern Recognition
International Conference on Machine Learning and Applications, 2002
Short-term forecasting of the likelihood of interference to groundwave users in the lowest part of the HF spectrum
2008 4th International IEEE Conference Intelligent Systems, 2008
In the design and performance evaluation of practical HF communication systems, it is essential t... more In the design and performance evaluation of practical HF communication systems, it is essential to use procedures that assess the detrimental effect of interference from other users in a near real time mode. These procedures can extend system capability to estimate interference background, in the context of real time channel evaluation (RTCE) in order to advise operators on typical interference
Predicting the Occupancy of the HF Amateur Service with Neural Network Ensembles
Lecture Notes in Computer Science, 2009
The Amateur Service is allocated approximately 3 MHz of spectrum in the HF band (3-30MHz) which i... more The Amateur Service is allocated approximately 3 MHz of spectrum in the HF band (3-30MHz) which is primarily used for long range communications via the ionosphere. However only a fraction of this resource is usually available due to unfavourable propagation conditions in the ionosphere imposed by solar activity on the HF channel. In this respect interference is considered a significant
A Neural Network Model for the Critical Frequency of the F2 Ionospheric Layer over Cyprus
Communications in Computer and Information Science, 2009
This paper presents the application of Neural Networks for the prediction of the critical frequen... more This paper presents the application of Neural Networks for the prediction of the critical frequency foF2 of the ionospheric F2 layer over Cyprus. This ionospheric characteristic (foF2) constitutes the most important parameter in HF (High Frequency) communications since it is used to derive the optimum operating frequency in HF links. The model is based on ionosonde measurements obtained over a
Osteoporosis Risk Assessment with Well-Calibrated Probabilistic Outputs
IFIP Advances in Information and Communication Technology, 2013
Feature selection has been recently used in the area of software engineering for improving the ac... more Feature selection has been recently used in the area of software engineering for improving the accuracy and robustness of software cost models. The idea behind selecting the most informative subset of features from a pool of available cost drivers stems from the hypothesis that reducing the dimensionality of datasets will significantly minimise the complexity and time required to reach to an estimation using a particular modelling technique. This work investigates the appropriateness of attributes, obtained from empirical project databases and aims to reduce the cost drivers used while preserving performance. Finding suitable subset selections that may cater improved predictions may be considered as a pre-processing step of a particular technique employed for cost estimation (filter or wrapper) or an internal (embedded) step to minimise the fitting error. This paper compares nine relatively popular feature selection methods and uses the empirical values of selected attributes record...
Software cost estimation is one of the prerequisite managerial activities carried out at the soft... more Software cost estimation is one of the prerequisite managerial activities carried out at the software development initiation stages and also repeated throughout the whole software life-cycle so that amendments to the total cost are made. In software cost estimation typically, a selection of project attributes is employed to produce effort estimations of the expected human resources to deliver a software product. However, choosing the appropriate project cost drivers in each case requires a lot of experience and knowledge on behalf of the project manager which can only be obtained through years of software engineering practice. A number of studies indicate that popular methods applied in the literature for software cost estimation, such as linear regression, are not robust enough and do not yield accurate predictions. Recently the dual variables Ridge Regression (RR) technique has been used for effort estimation yielding promising results. In this work we show that results may be fur...
Evolutionary Conformal Prediction for Breast Cancer Diagnosis
Conformal Prediction provides a framework for extending traditional machine learning algorithms, ... more Conformal Prediction provides a framework for extending traditional machine learning algorithms, in order to complement predictions with reliable measures of confidence. The provision of such measures is significant for medical diagnostic systems, as more informed diagnoses can be made by medical experts. In this paper, we introduce a conformal predictor based on genetic algorithms, and we apply our method on the Wisconsin breast cancer diagnosis (WBCD) problem. We give results in which we show that our method is efficient, in terms of accuracy, and can provide useful confidence measures.
Guest editors’ preface to the special issue on conformal prediction and its applications
Annals of Mathematics and Artificial Intelligence, 2014
Reliable Probability Estimates Based on Support Vector Machines for Large Multiclass Datasets
IFIP Advances in Information and Communication Technology, 2012
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Papers by Harris Papadopoulos