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Applying Balancing Techniques to Classify Biomedical Documents: An Empirical Study
In the last decade several text mining methods have been proposed to automate the process of searching and classifying information in on-line biomedical publications. However, results are not enough good mainly because of the unbalanced nature of the documents, with only a very small number of relevant papers to each user query. Due to most data mining and machine learning algorithms have a great difficult dealing with unbalanced data,this problem is taking center stage. Classification techniques such as support-vector machines (SVMs) have excellent performance for balanced data, but may fail when applied to unbalanced datasets. One of the most common techniques for dealing with this problem consists of changing the basic sampling methods including under-sampling, over-sampling and re-sampling. This article discusses the issues associated with classifying of unbalanced data, and analyze the effects of these balancing strategies on four different SVM kernels (lineal, sigmoid, exponential and polynomial kernels) using the TREC Genomics 2005 biomedical text public corpus. The experimental results show that normalized lineal and sigmoid kernels and the under-sampling balancing technique outperform the other approaches tested. Empirical tests are conducted using a new software tool named BioClass which is presented here.
Biomedical text mining, classification techniques, Support vector machine, kernels,
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