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

Abstract

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.

Traditional and deep‐based techniques for end‐to‐end automated karyotyping: A review

“…Wang et al (2009) proposed genetic algorithm‐based method to optimize the topology and proved that it improves performance. In spite of the fact that the previous endeavours of applying ANN for chromosome classification exhibited their appropriateness, they were primarily based on extraction and selection of handcrafted features (Lerner et al (1994); Moradi and Setarehdan (2006); Arora and Dhir (2016)). It is seen that geometrical features and band features are commonly used features by these classifiers.…”

Feature reduction scheme for anomaly‐based intrusion detection in wireless networks: Building of hybrid model

“…These works show that various fitness functions have been used for selecting the features by finding the relation between the features. [21][22][23][24] On the other hand, these feature selection and reduction techniques have been proved themselves as one of the main pillars of designing a security mechanism for intrusion detection in wireless networks. Reference 25 proposed an intrusion detection system using the feature deduction technique.…”

“…In this technique, the mutual information based function was guided by one of the optimizations, that is, genetic algorithm based approach for the optimized selection of features. These works show that various fitness functions have been used for selecting the features by finding the relation between the features 21‐24 …”

Preprocessing G-banded metaphase: towards the design of automated karyotyping

“…Automated detection of single isolated chromosomes and cluster of touching or overlapping chromosomes has been addressed in the literature. [21] proposed a system to classify the segmented chromosomes into five classes, using geometric features. Correlation-based feature selection (CFS) scheme and Classification via regression (CVR) classifier were respectively used for the feature selection and classification of the objects.…”