Toward Improving Classification of Real World Biomedical Articles
Kostas Fragos, Christos Skourlas · 2014
In this work, we propose a method to improve performance in biomedical article classification. We use Naïve Bayes and Maximum Entropy classifiers to classify real world biomedical articles. We describe a technique based on chi-square measure to discard irrelevant information from the data and to identify the most relevant keywords to the classification task. To improve classification performance, we used two merging operators, Max and Harmonic Mean proposed by Jongwoo et al (2010) to combine results of the two classifiers. The results show that the Maximum Entropy classifier shows the better performance at 500 top relevant keywords. It is also shown that combining the results of the two classifiers we can improve classification performance of real world biomedical data.