A novel technique: ensemble hybrid 1NN model using stacking approach
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ORIGINAL RESEARCH
A novel technique: ensemble hybrid 1NN model using stacking approach Preeti Nair1,3
•
Navneet Khatri2 • Indu Kashyap1
Received: 10 September 2017 / Accepted: 8 February 2018 Bharati Vidyapeeth’s Institute of Computer Applications and Management 2018
Abstract This paper proposes a novel hybrid classification model which has enhanced the performance of the standard kNN (k = 1) classification model significantly. In this study by the means of ensemble stacking approach kNN classification model and rotation forest classification model are hybridized as base classifiers and simple logistic classifier as the meta classification model. The performance of this proposed hybrid model was assessed using Accuracy and FMeasure. The model was compared with standard kNN and nine other classification models. The results showed that the proposed hybrid model has notably high performance than the other models. Keywords k nearest neighbor Rotation forest Simple logistic Hybrid classification model
1 Introduction The process of finding interesting patterns from enormous amounts of data is called data mining. Such rich and fascinating patterns can be valuable for large businesses and & Preeti Nair [email protected] Navneet Khatri [email protected] Indu Kashyap [email protected] 1
Manav Rachana University, Delhi Suraj Kund Road, Sector 43, Faridabad, Haryana 121004, India
2
Maharshi Dayanand University, Delhi Road, University Secretariat, Rohtak, Haryana 124001, India
3
T 2/1, DLF Phase 3, Gurgaon, Haryana 122002, India
for making smart decisions. This helps in improving customer relationship, developing marketing policies, improving sales and reduces costs. Data mining is a multidisciplinary field which bonds statistics, machine learning, artificial intelligence and database technologies to predict future from large data repositories. The data mining methods such as association, classification and clustering can be applied on various kinds of data such as database data, transactional data and data warehouse. The focus of this paper is on one of the techniques of data mining called classification. The process of sorting objects into similar groups is called classification. The classification process has two steps, first is the training step or the learning step where a classification model also known as a classifier finds correlations between the class labels and the features in a given dataset. In the second step, this classification model is supplied with test data to see the performance of the model. There are various application areas where classification can be useful like spam filtering, fraud detection, target marketing, customer attraction, customer retention, performance prediction, manufacturing, medical diagnosis etc. So there is a great need in the research field to improve the accuracy of the classifier. There are many classification models and much work has been carried out to improve the efficiency of these traditional models. Nearest neighbour is one of most popular c
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