A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

Introducing Undergraduate Students to Science

Understanding the scientific method fosters the development of critical thinking and logical analysis of information. Additionally, proposing and testing a hypothesis is applicable not only to science, but also to ordinary facts of daily life. Knowing the way science is done and how its results are published is useful for all citizens and mandatory for science students. A 60-h course was created to offer undergraduate students a framework in which to learn the procedures of scientific production and publication. The course`s main focus was biochemistry, and it was comprised of two modules. Module I dealt with scientific articles, and Module II with research project writing. Module I covered the topics: 1) the difference between scientific knowledge and common sense, 2) different conceptions of science, 3) scientific methodology, 4) scientific publishing categories, 5) logical principles, 6) deductive and inductive approaches, and 7) critical reading of scientific articles. Module II dealt with 1) selection of an experimental problem for investigation, 2) bibliographic revision, 3) materials and methods, 4) project writing and presentation, 5) funding agencies, and 6) critical analysis of experimental results. The course adopted a collaborative learning strategy, and each topic was studied through activities performed by the students. Qualitative and quantitative course evaluations with Likert questionnaires were carried out at each stage, and the results showed the students` high approval of the course. The staff responsible for course planning and development also evaluated it positively. The Biochemistry Department of the Chemistry Institute of the University of Sao Paulo has offered the course four times.