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- research-article
A Genetic Algorithm-based Auto-ML System for Survival Analysis
SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing•April 2024, pp 370-377• https://doi.org/10.1145/3605098.3635954Survival analysis methods aim to develop a model predicting the time passed until the occurrence of an event (e.g. death) for each subject. This requires coping with censored values of the target variable (time until the event), i.e., for some subjects, ...
- Article
Evaluating a New Genetic Algorithm for Automated Machine Learning in Positive-Unlabelled Learning
AbstractPositive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of a set of labelled positive instances and a set of unlabelled instances, where the latter can be either positive or ...
- article
Evaluating the Predictive Performance of Positive- Unlabelled Classifiers: a brief critical review and practical recommendations for improvement
ACM SIGKDD Explorations Newsletter, Volume 24, Issue 2•December 2022, pp 5-11 • https://doi.org/10.1145/3575637.3575642Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances. Whilst much work has been done proposing methods for PU learning, little has been ...
- research-article
Interpretable Ensembles of Classifiers for Uncertain Data With Bioinformatics Applications
IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume 20, Issue 3•May-June 2023, pp 1829-1841 • https://doi.org/10.1109/TCBB.2022.3218588Data uncertainty remains a challenging issue in many applications, but few classification algorithms can effectively cope with it. An ensemble approach for uncertain categorical features has recently been proposed, achieving promising results. It consists ...
- poster
GA-auto-PU: a genetic algorithm-based automated machine learning system for positive-unlabeled learning
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion•July 2022, pp 288-291• https://doi.org/10.1145/3520304.3528932Positive-Unlabeled (PU) learning is a growing field of machine learning that now consists of numerous algorithms; the number is now so large that considering an extensive manual search to select the best algorithm for a given task is impractical. As such,...
- poster
Nested trees for longitudinal classification
SAC '22: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing•April 2022, pp 441-444• https://doi.org/10.1145/3477314.3507240Longitudinal datasets contain repeated measurements of the same variables at different points in time. Longitudinal data mining algorithms aim to utilize such datasets to extract interesting knowledge and produce useful models. Many existing ...
- research-article
Prioritizing positive feature values: a new hierarchical feature selection method
Applied Intelligence, Volume 50, Issue 12•Dec 2020, pp 4412-4433 • https://doi.org/10.1007/s10489-020-01782-5AbstractIn this work we address the problem of feature selection for the classification task in hierarchical and sparse feature spaces, which characterize many real-world applications nowadays. A binary feature space is deemed hierarchical when its binary ...
- Article
An Evolutionary Algorithm for Learning Interpretable Ensembles of Classifiers
AbstractEnsembles of classifiers are a very popular type of method for performing classification, due to their usually high predictive accuracy. However, ensembles have two drawbacks. First, ensembles are usually considered a ‘black box’, non-...
- research-article
A robust experimental evaluation of automated multi-label classification methods
GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference•June 2020, pp 175-183• https://doi.org/10.1145/3377930.3390231Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effective methods were introduced, especially for traditional classification ...
- research-article
A Novel Feature Selection Method for Uncertain Features: An Application to the Prediction of Pro-/Anti-Longevity Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume 18, Issue 6•Nov.-Dec. 2021, pp 2230-2238 • https://doi.org/10.1109/TCBB.2020.2988450Understanding the ageing process is a very challenging problem for biologists. To help in this task, there has been a growing use of classification methods (from machine learning) to learn models that predict whether a gene influences the process of ...
- Article
Monotonicity Detection and Enforcement in Longitudinal Classification
AbstractLongitudinal datasets contain repeated measurements of the same variables at different points in time, which can be used by researchers to discover useful knowledge based on the changes of the data over time. Monotonic relations often occur in ...
- Article
Analysing the Overfit of the Auto-sklearn Automated Machine Learning Tool
Machine Learning, Optimization, and Data Science•September 2019, pp 508-520• https://doi.org/10.1007/978-3-030-37599-7_42AbstractWith the ever-increasing number of pre-processing and classification algorithms, manually selecting the best algorithm and their best hyper-parameter settings (i.e. the best classification workflow) is a daunting task. Automated Machine Learning (...
- article
An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features
Artificial Intelligence Review, Volume 50, Issue 2•August 2018, pp 201-240 • https://doi.org/10.1007/s10462-017-9541-yHierarchical feature selection is a new research area in machine learning/data mining, which consists of performing feature selection by exploiting dependency relationships among hierarchically structured features. This paper evaluates four hierarchical ...
- research-article
A Survey of Genetic Algorithms for Multi-Label Classification
2018 IEEE Congress on Evolutionary Computation (CEC)•July 2018, pp 1-8• https://doi.org/10.1109/CEC.2018.8477927In recent years, multi-label classification (MLC) has become an emerging research topic in big data analytics and machine learning. In this problem, each object of a dataset may belong to multiple class labels and the goal is to learn a classification ...
- research-article
Multi-objective genetic algorithms in the study of the genetic codes adaptability
Information Sciences: an International Journal, Volume 425, Issue C•January 2018, pp 48-61 • https://doi.org/10.1016/j.ins.2017.10.022Using a robustness measure based on values of the polar requirement of amino acids, Freeland and Hurst (1998) showed that less than one in one million random hypothetical codes are better than the standard genetic code. In this paper, instead of ...
- research-article
An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives
Expert Systems with Applications: An International Journal, Volume 85, Issue C•November 2017, pp 169-181 • https://doi.org/10.1016/j.eswa.2017.05.029An extensive evaluation of machine learning methods.Predictive performance not affected between Europe and the USA.Some linear relationships exists between error and climate.Machine learning methods outperform state-of-the-art. Regression problems ...
- research-article
Towards a method for automatically selecting and configuring multi-label classification algorithms
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion•July 2017, pp 1125-1132• https://doi.org/10.1145/3067695.3082053Given a new dataset for classification in Machine Learning (ML), finding the best classification algorithm and the best configuration of its (hyper)-parameters for that particular dataset is an open issue. The Automatic ML (Auto-ML) area has emerged to ...
- research-article
Instance-based classification with Ant Colony Optimization
Instance-based learning (IBL) methods predict the class label of a new instance based directly on the distance between the new unlabeled instance and each labeled instance in the training set, without constructing a classification model in the ...
- research-article
An Extensive Empirical Comparison of Probabilistic Hierarchical Classifiers in Datasets of Ageing-Related Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume 13, Issue 6•November 2016, pp 1045-1058 • https://doi.org/10.1109/TCBB.2015.2505288This study comprehensively evaluates the performance of five types of probabilistic hierarchical classification methods used for predicting Gene Ontology GO terms related to ageing. Of those tested, a new hybrid of a Local Hierarchical Classifier LHC ...
- article
Improving the interpretability of classification rules discovered by an ant colony algorithm: Extended results
Evolutionary Computation, Volume 24, Issue 3•Fall 2016, pp 385-409 • https://doi.org/10.1162/EVCO_a_00155Most ant colony optimization ACO algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner<inline-formula><inline-graphic xlink="...