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Xuesong Yan
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Weighted K-Nearest Neighbor Classification Algorithm Based on Genetic Algorithm Xuesong Yan
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 10: October 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

K-Nearest Neighbor (KNN) is one of the most popular algorithms for data classification. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different datasets. The traditional KNN text classification algorithm has limitations: calculation complexity, the performance is solely dependent on the training set, and so on. To overcome these limitations, an improved version of KNN is proposed in this paper, we use genetic algorithm combined with weighted KNN to improve its classification performance. and the experiment results shown that our proposed algorithm outperforms the KNN with greater accuracy. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.2534
Design and Analysis of Parallel MapReduce based KNN-join Algorithm for Big Data Classification Xuesong Yan
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 11: November 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i11.pp7927-7934

Abstract

In data mining applications, multi-label classification is highly required in many modern applications. Meanwhile, a useful data mining approach is the k-nearest neighbour join, which has high accuracy but time-consuming process. With recent explosion of big data, conventional serial KNN join based multi-label classification algorithm needs to spend a lot of time to handle high volumn of data.  To address this problem, we first design a parallel MapReduce based KNN join algorithm for big data classification. We further implement the algorithm using Hadoop in a cluster with 9 vitual machines. Experiment results show that our MapReduce based KNN join exhibits much higher performance than the serial one. Several interesting phenomenon are observed from the experiment results.
Research of Function Optimization Algorithm Qinghua Wu; Hanmin Liu; Yuxin Sun; Fang Xie; Jin Zhang; Xuesong Yan
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 4: August 2012
Publisher : Institute of Advanced Engineering and Science

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Traditional evolutionary algorithm trapped into the local minimum easily. Therefore, based on a simple evolutionary algorithm and combine the base ideology of orthogonal test then applied it to the population initialization, crossover operator, as well as the introduction of Inver-Over operator to prevent local convergence to form a new evolutionary algorithm. Through the series of numerical experiments, the new algorithm has been proved is efficiency for function optimization. DOI: http://dx.doi.org/10.11591/telkomnika.v10i4.877
Co-Authors Fang Xie Hanmin Liu Jin Zhang Qinghua Wu Yuxin Sun