Optimising Genes Selection with Greedy Heuristic Fuzzy Clustering for Binary Classification Problems

With the advancement of the microarray data, reduction in the data dimensions becomes a research hotspot. High-dimensional datasets need to be pre-processed using data reduction techniques. Features selection techniques are used to handle the dimensionality problem. Clustering techniques are also used to reduce the dimensions of data. It selects features highly correlated to the class labels, while less correlated among the features. In this paper, we proposed a new method called the Greedy Heuristic Fuzzy Clustering (GHFClust), which can be used in high dimensional datasets to improve the accuracy and reduce the high dimensionality problems. In this study, the minimum subset of features is selected using the greedy approach, in which interquartile range and relative covering analysis are used. For the remaining data, the fuzzy-c-means clustering technique is used. The results show that the GHFClust has a higher accuracy rate compared to the other methods using benchmark datasets.