Dimensionality reduction based on the classifier models: Performance Issues in the prediction of Lung cancer

Title

Dimensionality reduction based on the classifier models: Performance Issues in the prediction of Lung cancer

Creator

Balachandran K.; Anitha R.

Description

Dimensionality reduction is an essential feature to reduce the complexity of the computations in the large data set environment. When handling large quantum of medical data set, as in the case like, Lung cancer prediction, based on symptoms and Risk factors, number of attributes/ dimensions pose a major challenge. Here in this study an attempt is made to compare the performance of the attribute selection models prior and after applying the classifier models. A total of 16 classifier models are chosen, which are based on statistical, rule based, logic based and artificial Neural network approaches. Feature set selection and ranking of attributes are done based on individual models. Confusion matrix of the models before and after dimensionality reduction is computed. Based on the confusion matrix result the models are compared and based on the performance optimal model is chosen. It is found that Multi-layer perceptron based artificial neural network model gives better performance compared to other approaches. 2012 IEEE.

Source

2012 CSI 6th International Conference on Software Engineering, CONSEG 2012

Date

2012-01-01

Subject

Artificial Neural Network; classifier; Dimensionality reduction; Lung cancer

Coverage

Balachandran K., Computer Science and Engineering Department, Christ University, Bangalore, Karnataka, India; Anitha R., KSR College of Technology, Tirunchengodu, Tamil Nadu, India

Rights

Restricted Access

Relation

ISBN: 978-146732174-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/CONSEG.2012.6349511

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870822856&doi=10.1109%2fCONSEG.2012.6349511&partnerID=40&md5=b6e563fb8bab4d08e137adae04963a6f