📊 Classification Methods
Research on probabilistic classifiers, ensemble methods, feature selection, and classification from time-series data, with applications to anomaly detection, remote sensing, and energy systems.
Classification is one of the central tasks in machine learning: given a set of observations with known class labels, learn a model that can correctly classify new, unseen observations. My research on classification methods has developed novel classifiers based on probabilistic graphical models, ensemble methods, and evolutionary algorithms, with applications to remote sensing, energy systems, building management, and neuroscience.
Probabilistic Classifiers
Bayesian Network Classifiers
Development and evaluation of Bayesian network classifiers, from the simple naïve Bayes model to more expressive general Bayesian network classifiers. Research on how the structure of the Bayesian network affects classification accuracy, and on learning algorithms that optimize the structure specifically for classification rather than density estimation.
Vine Copula Classification
Application of vine copula models to classification tasks. Vine copulas provide a flexible framework for modeling the joint distribution of continuous features, allowing classifiers to capture complex multivariate dependency structures without making strong distributional assumptions.
Searchlight Classification for Brain Decoding
Application of classification methods to brain decoding, using searchlight analysis to identify which brain regions carry information about specific stimuli or cognitive states. Development of domain adaptation methods to transfer classification models across different experimental paradigms (e.g., visual perception to mental imagery).
Ensemble Methods
Random Vector Functional Link Forests
Research on random vector functional link (RVFL) forests and extreme learning forests applied to UAV automatic target recognition. RVFL networks are single-hidden-layer networks with randomly initialized and fixed input weights; forests of RVFL networks provide competitive performance with much lower training cost than deep learning alternatives.
Stacking LLM Predictions for Classification
Stacking (ensemble learning) applied to the predictions of multiple large language models for anomaly classification. By combining the predictions of diverse LLM models through a meta-learner, accuracy and explainability are improved compared to using any individual model alone.
Feature Selection
Filter-Based Feature Selection for Multi-target Regression
Development of filter method-based feature selection for multi-target regression problems with unattributed-identity data (where the correspondence between input and output samples is partially unknown). Research on how different filter measures handle the multi-target setting and the challenges posed by identity ambiguity.
Feature Selection for Anomaly Classification
Feature selection methods for anomaly classification in building management systems. Research on how to identify the most relevant features for classifying faults and anomalies in fan coil units and other building components, with an emphasis on causality and explainability of the selected features.
Time-Series Classification
Classification of Household Devices from Energy Time Series
Classification of household devices from electricity usage time-series data (non-intrusive load monitoring). Research on the impact of different imputation methods on classification accuracy and on the features that best characterize different device types from their electricity consumption patterns.
Fault Classification in Building Systems
Classification of faults in fan coil units from multivariate time-series sensor data. Research on the impact of temporal features and the choice of time-series representation on fault classification accuracy. Development of methods that exploit temporal dependencies in the data for more accurate fault detection and diagnosis.
Applications
Sand Dune Detection on Mars
Detection of sand dunes on Mars using a vine copula-based classification approach applied to orbital imagery. This application demonstrates the versatility of copula-based classifiers for remote sensing problems where the distribution of image features is non-Gaussian and exhibits complex multivariate dependencies.
Award Price Estimation for Public Procurement
Application of machine learning classification and regression algorithms to estimate the award price in public procurement auctions. Case study with tender data from Spain, examining how well different machine learning models can predict procurement outcomes from available features of the tender.
Selected Publications
- Carrera D, Santana R and Lozano JA (2019). Detection of sand dunes on Mars using a regular vine-based classification approach. Knowledge-Based Systems.
- Carrera D, Santana R and Lozano JA (2016). Vine copula classifiers for the mind reading problem. Progress in Artificial Intelligence.
- Santana R, Bielza C and Larrañaga P (2012). Regularized logistic regression and multi-objective variable selection for classifying MEG data. Biological Cybernetics.
- Santana R et al. (2013). Classification of neocortical interneurons using affinity propagation. Front. Neural Circuits.
- Santana R et al. (2015). Multi-view classification of psychiatric conditions based on saccades. GECCO 2015.
- Santana R (2013). A detailed investigation of classification methods for vowel speech imagery recognition. ESANN 2013.
- Santana R (2015). Supervised classification of vowel speech imagery. ESANN 2015.
- Garciarena U and Santana R (2017). An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers. Expert Systems with Applications.
- Vadillo J and Santana R (2019). Universal Adversarial Examples in Speech Command Classification. ICML Workshop 2019.
- Montenegro M, Marafioti A, Santana R, Alcaide JB, Bolaños M and Cuayahuitl H (2019). Data generation approaches for topic classification in multilingual spoken dialog system. INTERSPEECH 2019.
- Montenegro M, Santana R, Bolaños M and Cuayahuitl H (2020). Transfer learning in hierarchical dialogue topic classification with neural networks. IberSPEECH 2020.
- Zheng D, Guo K and Santana R (2015). Comparison of Classification Methods for EEG-based Emotion Recognition. ISNN 2015.