A parallel and distributed genetic-based learning classifier system with application in human electroencephalographic signal classification

University of Technology, Sydney. Faculty of Engineering.Genetic-based Learning Classifier Systems have been proposed as a competent technology for the classification of medical data sets. What is not known about this class of system is twofold. Firstly, how does a Learning Classifier System (LCS) perform when applied to the single-step classification of multiple-channel, noisy, artefact-inclusive human EEG signals acquired from many participants? Secondly and more importantly, is how the learning classifier system performs when incorporated with migration strategies, inspired by multi- deme, coarse-grained Parallel Genetic Algorithms (PGA) to provide parallel and distributed classifier migration? This research investigates these open questions and concludes, subject to the considerations herein, that these technological approaches can provide competitive classification performance for such applications. We performed a preliminary examination and implementation of a parallel genetic algorithm and hybrid local search PGA using experimental methods. The parallelisation and incorporation of classical local search methods into a genetic algorithm are well known methods for increasing performance and we examine this. Furthermore, inspired by the significant improvements in convergence velocity and solution quality provided by the multi- deme, coarse-grained Parallel Genetic Algorithm, we incorporate the method into a learning classifier system with the aim of providing parallel and distributed classifier migration. As a result, a unique learning classifier system (pXCS) is proposed that improves classification accuracy, achieves increased learning rates and significantly reduces the classifier population during learning. It is compared to the extended learning Classifier System (XCS) and several state of the art non-evolutionary classifiers in the single-step classification of noisy, artefact- inclusive human EEG signals, derived from mental task experiments conducted using ten human participants. We also conclude that establishing an appropriate migration strategy is an important cause of pXCS learning and classification performance. However, an inappropriate migration rate, frequency or selection:replacement scheme can reduce performance and we document the factors associated with this. Furthermore, we conclude that both EEG segment size and representation both have a significant influence on classification performance. In effect, determining an appropriate representation of the raw EEG signal is tantamount to the classification method itself. This research allows us to further explore and incorporate pXCS evolved classifiers derived from multi-channel human EEG signals as an interface in the control of a device such as a powered wheelchair or brain-computer interface (BCI) applications

Interpretable Low-Rank Document Representations with Label-Dependent Sparsity Patterns

In context of document classification, where in a corpus of documents their label tags are readily known, an opportunity lies in utilizing label information to learn document representation spaces with better discriminative properties. To this end, in this paper application of a Variational Bayesian Supervised Nonnegative Matrix Factorization (supervised vbNMF) with label-driven sparsity structure of coefficients is proposed for learning of discriminative nonsubtractive latent semantic components occuring in TF-IDF document representations. Constraints are such that the components pursued are made to be frequently occuring in a small set of labels only, making it possible to yield document representations with distinctive label-specific sparse activation patterns. A simple measure of quality of this kind of sparsity structure, dubbed inter-label sparsity, is introduced and experimentally brought into tight connection with classification performance. Representing a great practical convenience, inter-label sparsity is shown to be easily controlled in supervised vbNMF by a single parameter

Chi-square-based scoring function for categorization of MEDLINE citations

Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain. Results: Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method by comparing it to three machine learning algorithms (support vector machines, decision trees, na\"ive Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine learning algorithms. Conclusions: We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA literature-based discovery support system as a preprocessor for gene symbol disambiguation process.Comment: 34 pages, 2 figure