Open AccessA Research Travelogue on Classification Algorithms using R Programming
Author(s) -
S.Nagaparameshwara chary*,
B Rama
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d9014.118419
Subject(s) - computer science , machine learning , artificial intelligence , curse of dimensionality , support vector machine , set (abstract data type) , task (project management) , process (computing) , decision tree , domain (mathematical analysis) , data set , statistical classification , representation (politics) , genetic programming , k nearest neighbors algorithm , data mining , mathematics , mathematical analysis , management , politics , political science , law , economics , programming language , operating system
Classification is a machine learning task which consists in predicting the set association of unclassified examples, whose label is not known, by the properties of examples in a representation learned earlier as of training examples, that label was known. Classification tasks contain a huge assortment of domains and real world purpose: disciplines such as medical diagnosis, bioinformatics, financial engineering and image recognition between others, where domain experts can use the model erudite to sustain their decisions. All the Classification Approaches proposed in this paper were evaluate in an appropriate experimental framework in R Programming Language and the major emphasis is on k-nearest neighbor method which supports vector machines and decision trees over large number of data sets with varied dimensionality and by comparing their performance against other state-of-the-art methods. In this process the experimental results obtained have been verified by statistical tests which support the better performance of the methods. In this paper we have survey various classification techniques of Data Mining and then compared them by using diverse datasets from “University of California: Irvine (UCI) Machine Learning Repository” for acquiring the accurate calculations on Iris Data set.