A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era

Zhu Fangdong; Chen Wen; Yang Hanli; Li Tao; Yang Tao; Zhang Fan

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A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era

机译：大数据时代的一类分类快速负选择算法

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摘要

Negative selection algorithm (NSA) is an important kind of the one-class classification model, but it is limited in the big data era due to its low efficiency. In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the traditional " Random-Discard" model to the " Computing-Designated" model by VorNSA. Furthermore, we present an immune detection process of VorNSA underMap/Reduce framework (VorNSA/MR) to further reduce the time consumption on massive data in the testing stage. Theoretical analyses show that the time complexity of VorNSA decreases from the exponential level to the logarithmic level. Experiments are performed to compare the proposed technique with other NSAs and one-class classifiers. The results show that the time cost of the VorNSA is averagely decreased by 87.5% compared with traditional NSAs in UCI skin dataset.

机译：负选择算法（NSA）是一类分类模型中的一种重要类型，但由于其效率低而在大数据时代受到了限制。在本文中，我们提出了一种基于Voronoi图的新NSA：VorNSA。 VorNSA将检测器生成过程的方案从传统的“随机丢弃”模型更改为“计算指定”模型。此外，我们提出了Map / Reduce框架（VorNSA / MR）下VorNSA的免疫检测过程，以进一步减少测试阶段对海量数据的时间消耗。理论分析表明，VorNSA的时间复杂度从指数级降低到对数级。进行实验以将所提出的技术与其他NSA和一类分类器进行比较。结果表明，在UCI皮肤数据集中，与传统的NSA相比，VorNSA的时间成本平均降低了87.5％。

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《Mathematical Problems in Engineering》 |2017年第6期|3956415.1-3956415.7|共7页
作者
Zhu Fangdong; Chen Wen; Yang Hanli; Li Tao; Yang Tao; Zhang Fan;
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Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China;

Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China|Sichuan Univ, Coll Cybersecur, Chengdu 610065, Peoples R China;

Chongqing Univ Technol, Chongqing 400054, Peoples R China;

Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China|Sichuan Univ, Coll Cybersecur, Chengdu 610065, Peoples R China;

Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China;

Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China|Chengdu Univ Informat Technol, Chengdu 610225, Peoples R China;

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1. The Dual Negative Selection Algorithm Based on Pattern Recognition Receptor Theory and Its Application in Two-class Data Classification [J] . Xufei Zheng1, Yanhui Zhou1, Yonghui Fang2 Journal of Computers . 2013,第8期

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2. One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm [J] . Zurui Ao, Yanjun Su, Wenkai Li, Remote Sensing . 2017,第10期

机译：使用存在和背景学习算法对城市机载LiDAR数据进行一类分类
3. One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm [J] . Zurui Ao, Yanjun Su, Wenkai Li, Remote Sensing . 2017,第10期

机译：使用存在和背景学习算法对城市机载LiDAR数据进行一类分类
4. One-class Bearing Fault Detection using Negative Clone Selection Algorithm [C] . Tao Xinmin, Du Baoxiang, Xu Yong Annual Conference of the IEEE Industrial Electronics Society . 2008

机译：一流的轴承故障检测使用负克隆选择算法
5. Genetic algorithms for feature selection and classification of complex chromatographic and spectroscopic data [D] . Mirjankar, Nikhil Suresh 2012

机译：遗传算法用于复杂色谱和光谱数据的特征选择和分类
6. Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm [O] . Shayma Mustafa Mohi-Aldeen, Radziah Mohamad, Safaai Deris 2020

机译：基于混合负选择算法和遗传算法的最优路径测试数据生成
7. A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era [O] . Fangdong Zhu, Wen Chen, Hanli Yang, 2017

机译：大数据时代单级分类的快速负选择算法

A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era

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