HSEC：基于聚类的启发式选择性集成

南京大学学报(自然科学版) ›› 2018, Vol. 54 ›› Issue (1): 116–.

HSEC：基于聚类的启发式选择性集成

郑丽容1，洪志令2*

出版日期:2018-01-31 发布日期:2018-01-31
作者简介: 1.厦门大学计算机科学系，厦门，361005；2.厦门大学软件学院，厦门，361005
基金资助:
基金项目：国家自然科学基金(31200769)
收稿日期：2017－12－20
*通讯联系人，E－mail：hongzl@xmu.edu.cn

HSEC：Clustering based heuristic selelctive ensemble learning algorithm

Zheng Lirong1，Hong Zhiling2*

Online:2018-01-31 Published:2018-01-31
About author:1.School of Information Science and Engineering，Xiamen University，Xiamen，361005，China；
2．Software School of Xiamen University，Xiamen，361005，China

摘要/Abstract

摘要： 提出一种基于聚类的启发式选择性集成学习算法．集成学习通过组合多个弱分类器获得比单一分类器更好的学习效果，把多个弱分类器提升为一个强分类器．理论上来说弱分类器的个数越多，组合的模型效果越好，但是随着弱分类器的增多，模型的训练时间和复杂度也随之递增．通过聚类的方法去除相似的弱分类器，一方面有效降低模型的复杂度，另一方面选出差异性较大的弱分类器作为候选集合．之后采用启发式的选择性集成算法，对弱分类器进行有效的组合，从而提升模型的分类性能．同时采用并行的集成策略，提高集成学习选取最优分类器子集效率，可以有效地减少模型的训练时间．实验结果表明，该算法较传统方法在多项指标上都有着一定的提升．

Abstract: This paper introduces a clustering－based heuristic selective ensemble algorithm，called clustering based heuristic selective ensemble learning algrothm(HSEC)．Ensemble learning obtains a better predictive through combining multiple weak models.However，with the increasing amount of the weak model，the complexity and training time become more complex.We use a clustering selective method to eliminate similarity classifiers to decrease the amount of weak models.Then，we select the best sequence set of classifiers based on the heuristic selective ensemble algorithm.We use multiprocessors to train model to solve the inefficiency of the selection classifier in the previous study for integration of learning，which could improve efficiency greatly.The experimental results show that the HSEC algorithm has made a certain boost in some indicators comparing with traditional classification algorithm.

郑丽容1，洪志令2*. HSEC：基于聚类的启发式选择性集成[J]. 南京大学学报(自然科学版), 2018, 54(1): 116–.

Zheng Lirong1，Hong Zhiling2*. HSEC：Clustering based heuristic selelctive ensemble learning algorithm[J]. Journal of Nanjing University(Natural Sciences), 2018, 54(1): 116–.

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