Analyzing gene expression data: Fuzzy decision tree algorithm applied to the classification of cancer data

In data mining, decision tree algorithms are very popular methodologies since the algorithms have a simple inference mechanism and provide a comprehensible way to represent the model in the form of a decision tree. Over the past years, fuzzy decision tree algorithms have been proposed in order to provide a way to handle uncertainty in the data collected. Fuzzy decision tree algorithms have shown to outperform classical decision tree algorithms. This paper investigates a fuzzy decision tree algorithm applied to the classification of gene expression data. The fuzzy decision tree algorithm is compared to a classical decision tree algorithm as well as other well-known data mining algorithms commonly applied to classification tasks. Based on the five data sets analyzed, the fuzzy decision tree algorithm outperforms the classical decision tree algorithm. However, compared to other commonly used classification algorithms, both decision tree algorithms are competitive, although both do not reach the accuracy values of the best performing classifier.

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