Research

My research is focused on Machine Learning, the main topics of interest are:

• Current research
  • Semi-supervised classification
    • The semi-supervised learning is a relatively new field in Machine Learning.
      • In contrast with supervised learning, in which we only use the labelled
      • instances, the main idea of the semi-supervised learning is to learn from
      • both labelled and unlabelled data in a given problem. By using the
      • unlabelled data, we aim to improve the supervised learning process. In the
      • context of the semi-supervised learning, we focus on the semi-supervised
      • classification task, which consists of classifying unseen instances
      • according to the knowledge acquired from a semi-supervised training
      • algorithm. So, in order to address such task, we propose an ensemble-based
      • technique, in which each ensemble component increases its classification
      • accuracy by using the knowledge retrieved from other classifiers. In this
      • sense, we intend to improve the current results in the literature.
  • Ensemble Learning
  • Evolutionary Computation
  • Meta-learning
  • Clustering
• M.Phil
  • Abstract of M.Phil Dissertation
    • The amount of gene expression data has been exponentially growing in recent years
      • due to the new Molecular Biology technologies, that allow measuring the expression of
      • thousands of genes at once. The computational analysis of such data is of major importance
      • in Biology and Medicine, it allows, for example, the recovery of new biologically
      • and clinically signicant cancer classes and the identification of new functions of genes.
      • The unsupervised Machine Learning techniques take part in the experts' data analysis
      • methodology. There is a variety of data clustering algorithms, each one tends to cluster
      • the data in a specific way. The choice of such algorithms is fundamental to the clustering
      • quality and, therefore, it's important to the proper results analysis. We propose a
      • meta-learning methodology for the clustering algorithms selection in the context of cancer
      • cells gene expression. So far, meta-learning has been used only for supervised learning
      • algorithms, we extended that concept for unsupervised learning. We used datasets from
      • different cancer microarray experiments. We extracted relevant characteristics from each
      • dataset in order to employ them in the learning of Neural Networks, k-Nearest Neighbors
      • and Support Vectors Machine, used as meta-learners. These methods were used as
      • learning systems to predict the performance ranking of clustering algorithms, as well as
      • to select the best algorithm, according to the dataset characteristics. We performed a
      • set of experiments in order to validate the use of each meta-learner. In this context, we
      • showed that, in average, the Support Vector Machines suggested rankings that are more
      • correlated with the ideal ranking than the ones obtained by the default ranking. We could
      • propose a novel approach, which can be extended to data from other contexts, so it can
      • be the stating point for other works.
      • Keywords: Meta-learning, Unsupervised Learning, Gene Expression.