基于判断聚合的分布式数据挖掘分类算法研究

doi:10.11896/JsJkx.190700143

摘要/Abstract

摘要： 随着互联网的发展和云计算技术的广泛应用,许多数据存储在不同的服务器上,分布式数据挖掘技术应运而生。智能agent在各自的站点上得到部分挖掘结果,分布式数据挖掘可以将这些部分的挖掘结果聚合成为全局的结果。文中主要处理的是分布式数据挖掘过程中的分类问题,针对一些特征的数据分别存储于不同的数据源上,提出了一种基于判断聚合模型的分类算法。该算法中每一个agent要对一个案例属于某一个目标类的可能性进行判断,然后利用判断聚合模型将这些agent的判断进行聚合,形成全局的分类结果。基于判断聚合模型的分类算法将逻辑和社会选择理论的技术应用于解决分布式数据挖掘的分类问题,这种新的算法不需要大规模地传输和转化数据,节省了传输成本,提高了分类效率,同时有效地保护了数据的安全性。

关键词: 多主体系统, 分布式数据挖掘, 逻辑, 判断聚合模型, 算法

Abstract: With the development of Internet and the wide application of cloud computing,many data sets are stored on different servers,and the distributed data mining comes into being.Each agent gets partial data mining results on its respective site,and distributed data mining could aggregate this part of mining results into a global decision.This paper is focused on the classification issue in the process of distributed data mining.Aiming at some specific data are stored in difference data source,this paper puts forward a classification algorithm based on the Judgment aggregation model.Each agent should give its Judgment whether a new case belongs to a certain target class,and then use the Judgment aggregation model to aggregate the Judgments of these agents to form a global classification.This algorithm combines logic and social choice theory technologies together and applies them to the classification problem in distributed data mining.It doesn’t need to transfer and transform the data on a large scale,thus saving the transmission cost and improving the efficiency of classification.At the same time,it effectively protects the data security.

Key words: Algorithm, Distributed data mining, Judgment aggregation model, Logic, Multi-agent system

中图分类号:

TP18

李莉. 基于判断聚合的分布式数据挖掘分类算法研究[J]. 计算机科学, 2020, 47(6A): 450-456. https://doi.org/10.11896/JsJkx.190700143

LI Li. Classification Algorithm of Distributed Data Mining Based on Judgment Aggregation[J]. Computer Science, 2020, 47(6A): 450-456. https://doi.org/10.11896/JsJkx.190700143

参考文献

[1] CAO L,WEISS G,YU P S.A brief introduction to agent mining .Autonomous Agent and Multi-Agent Systems,2012,25(3):419-424.
[2] MODI P J,SHEN W M.Collaborative multiagent learning for classification tasks//Proceedings of the Fifth International Conference on Autonomous Agent.ACM,2001:37-38.
[3] SANTANA L E A,CANUTO A M P,JUNIOR J C X,et al.A comparative analysis of data distribution methods in an agent-based neural system for classification tasks//Sixth International Conference on Hybrid Intelligent Systems(HIS’06).IEEE,2006:9-9.
[4] KARGUPTA H,HUANG W,SIVAKUNMAR K,et al.Distributed clustering using collective principal component analysis .Knowledge and Information Systems,2001,3(4):422-448.
[5] PARK B H,KARGUPTA H.Distributed data mining:Algorithms,systems,and applications //Data Mining Handbook.2002. MATATOV N,ROKACH L,MAIMON O.Privacy-preserving data mining:A feature set partitioning approach .Information Sciences,2010,180(14):2696-2720.
[6] GAERTNER W.A primer in social choice theory:Revised edition .Oxford University Press,2009.
[7] BRANDT F,CONITZER V,ENDRISS U,et al.Handbook of Computational Social Choice.Cambridge University Press,2016.
[8] ARROW K J.Social choice and individual values.Yale University Press,1963.
[9] KORNHAUSER L A,SAGER L G.Unpacking the court .The Yale Law Journal,1986,96(1):82-117.
[10] LIST C,POLAK B.Introduction to Judgment aggregation.Journal of economic theory,2010,145(2):441-466.
[11] LANG J,PIGOZZI G,SLAVKOVIK M,et al.Judgment aggregation rules based on minimization//Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Know-ledge.ACM,2011:238-246.
[12] LANG J,SLAVKOVIK M.How hard is it to compute maJority-preserving Judgment aggregation rules//Proceedings of the Twenty-first European Conference on Artificial Intelligence.IOS Press,2014:501-506.
[13] LANG J,PIGOZZI G,SLAVKOVIK M,et al.A partial taxonomy of Judgment aggregation rules and their properties .Social Choice and Welfare,2017,48(2):327.
[14] LANG J,MENGIN J,XIA L.Voting on multi-issue domains with conditionally lexicographic preferences .Artificial Intelligence,2018,265:18-44.
[15] LI L,TANG X.A solution to the cold-start problem in recommender systems based on social choice theory //Intelligent and Evolutionary Systems.Springer,Cham,2016:267-279.
[16] LI L I,NIU B,TANG S X.Online marketing research based on social choice theory and cloud computing//2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC).IEEE,2015:391-396.

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