Correlation-based feature selection and classification via regression of segmented chromosomes using geometric features.

The genetic defects in the humans are uncovered by studying the chromosomes, as they are the genetic information carriers. They are non-rigid objects and they appear in different orientations when they are imaged. To find out the genetic defects, the chromosomes are pre-processed so that they are not touching, overlapping, and bent, and the noise is also discarded. The presence of bends, overlaps, or touches makes it difficult to uncover the genetic abnormalities. So there is a need for development of an efficient technique to classify the segmented chromosomes into different types and then pre-process them in order to correct their orientation. In this work, a hybrid classification technique based upon correlation-based feature selection and classification via regression approach, which will classify the segmented chromosomes into five categories viz; straight, overlapping, bent, touching, or noise is presented. The performance evaluation has been done using 1592 segmented chromosomes from Advance Digital Imaging Research data set. The over-all accuracy of 94.78 % has been obtained for the five class problem. The performance of the proposed classifier has been compared with Bayes Net, Naïve Bayes, Radial Bias Feed Forward Network, and k-nearest-neighbour classifiers. Based upon this categorization, different pre-processing techniques will be applied to correct the orientation of the chromosomes.