Feature selection based on the classifier models: Performance issues in the prediagnosis of lung cancer

Title

Feature selection based on the classifier models: Performance issues in the prediagnosis of lung cancer

Creator

Balachandran K.; Anitha R.

Description

Dimensionality reduction is generally carried out to reduce the complexity of the computations in the large data set environment by removing redundant or de-pendent attributes. For the Lung cancer disease prediction, in the pre-diagnosis stage, symptoms and risk factors are the main information carriers. Large number of symptoms and risk attributes poses major challenge in the computation. 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 preferred based on relevancy of the models with respect to the data types 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. Based on the confusion matrix parameters the models prediction outcomes are found out in the supervisory training mode. The Confusion matrix of the models before and after dimensionality reduction is computed. Models are compared based on weighted Reader Operator Characteristics. Normalized weights are assigned based for the result of individual models and predictive model is developed. Predictive models performance is studied with target under supervised classifier model and it is observed that it is tallying with the expected outcome. 2005 - 2014 JATIT & LLS. All rights reserved.

Source

Journal of Theoretical and Applied Information Technology, Vol-59, No. 3, pp. 549-555.

Date

2014-01-01

Publisher

Asian Research Publishing Network (ARPN)

Subject

Artificial neural network; Classifier; Data mining; Feature selections; Lung cancer; Pre-diagnosis

Coverage

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

Rights

Restricted Access

Relation

ISSN: 19928645

Format

Online

Language

English

Type

Article

Identifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893519101&partnerID=40&md5=05224267a85ff96196d7ae3c76bdaa3f