Main Article Content
Feature selection is played vital role for classification algorithmsin the machine learning. In real time data mining process, irrelevant features which are available in the dataset may decrease the accuracy level of the classification algorithms. The selection ofthe appropriateand relevant features of the dataset in classification problems is the important role in data mining. The aim of the research work is to increase accuracy level of the classification algorithmsusing feature selection technique with different domain datasets.This paper also comparesaccuracy of different classification algorithms with and without feature selection method. The classification algorithms such as Bayesian Net, Naïve Bayes, Multi Layer Perception, logistic regression, J48 and Random Forestare used with feature selection methodusing different domain datasetssuch as Breast Cancer, Glass, Iris and Weather for comparison.This experiment is done with the help of Weka tool and datasetsin the machine learning repository. Feature selectiontechniques are effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy and improving result comprehensibility. However, the recent increase of dimensionality of data poses severe challenge to many existing feature selection techniques with respect to efficiency and effectiveness.
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.