Application design and analysis of different hybrid intelligent techniques

Abstract

Different classical and hybrid intelligent techniques have evolved over the years for the purpose of clustering different datasets. In the machine learning environment, among the different techniques in vogue, we interchangeably used the terms classification, regression and clustering for the inductive learning. The DIKW process of knowledge engineering has been extended and influenced by different intelligent techniques, hybrid in nature.

In this article, we propose a hybrid model of Fuzzy C Means with K-nearest neighbour(FCM-KNN) to classify different datasets. In the proposed model, we used one input layer, two middle layers for processing the input files and one output layer for visualization and data collection. We compare the same with Naive Bayes Learning and SVM. Out of three discussed machine learning techniques, Naive Bayes and SVM are supervised in nature and FCM-KNN is an unsupervised classification techniques.

Results of the proposed models are demonstrated on a real-life dataset using KNIME model design. A comparative study with the results obtained with the proposed architecture outperforms the other two models, viz. Naive Bayes and SVM, in terms of correct computation percentage.

Keywords

Classification regression clustering fuzzy c means Support Vector Machine (SVM)Naive Bayes K-Nearest Neighbour KNIME model design big data KDD

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