文章基本信息

标题：A Skyline-Based Decision Boundary Estimation Method for Binominal Classification in Big Data
本地全文：下载
作者：Christos Kalyvas ; Manolis Maragoudakis
期刊名称：Computation
电子版ISSN：2079-3197
出版年度：2020
卷号：8
期号：3
页码：80-101
DOI：10.3390/computation8030080
出版社：MDPI Publishing
摘要：One of the most common tasks nowadays in big data environments is the need to classify large amounts of data. There are numerous classification models designed to perform best in different environments and datasets, each with its advantages and disadvantages. However, when dealing with big data, their performance is significantly degraded because they are not designed—or even capable—of handling very large datasets. The current approach is based on a novel proposal of exploiting the dynamics of skyline queries to efficiently identify the decision boundary and classify big data. A comparison against the popular k-nearest neighbor (k-NN), support vector machines (SVM) and naïve Bayes classification algorithms shows that the proposed method is faster than the k-NN and the SVM. The novelty of this method is based on the fact that only a small number of computations are needed in order to make a prediction, while its full potential is revealed in very large datasets.
关键词：classification; skyline; big data; decision boundary classification ; skyline ; big data ; decision boundary