An Empirical Analysis of Classification Trees Algorithm for Protein Datsets
Main Article Content
Abstract
In general, the Classification tree can be used to predict membership of cases or objects in the classes of a categorical dependent variable from their measurements on one or more predictor variables. Generally, Classification tree analysis is one of the major techniques used in so-called Data Mining. In this paper we are analyzing the performance of 4 classifiers trees algorithms namely J48 Decision tree, Naïve Bayes Tree, Random Forest and Random tree. In this article we used protein datasets namely the dengue virus and the Superoxide Dismutase1 (SOD1) protein datasets for calculating the performance by using the cross validation parameter. And finally we performed the comparative analysis based on the factors such as the classification accuracy, performance and error rate measures on all the algorithms.
Â
Keywords: Data Mining, Classification, J48 Decision Tree, Random Forest, Random Tree, NaïveBayes Tree, Dengue, SOD1 datasets.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.