Attribute selection methods comparison for classification of diffuse large B-cell lymphoma

The use of data mining techniques has helped to solve many problems in the rapidly growing field of bioinformatics. Despite that, the presence of thousands of attributes makes the results unclear and also contributes to the decrease of the accuracy of the classifier used. This paper presents a comparison of the use of various attribute selection methods aiming to reduce the number of genes to be searched. The results show that most of the combinations from search algorithms and evaluation algorithms within the attribute selection algorithm work well, reducing the number of attributes and leading to improved classification rates.