Immune robust regression analysis for data set of multiple models

doi:10.11772/j.issn.1001-9081.2014.08.2285

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (8): 2285-2290.DOI: 10.11772/j.issn.1001-9081.2014.08.2285

• Artificial intelligence • Previous Articles Next Articles

Immune robust regression analysis for data set of multiple models

XU Xuesong¹,SHU Jian²

1. School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang Jiangxi 330013, China;
2. Academic Affairs Office, East China Jiaotong University, Nanchang Jiangxi 330013, China

Received:2014-02-20 Revised:2014-03-26 Online:2014-08-10 Published:2014-08-01
Contact: XU Xuesong

多模型数据集的免疫鲁棒回归分析

徐雪松¹,舒俭²

1. 华东交通大学电气与电子工程学院，南昌330013
2. 华东交通大学教务处，南昌330013

通讯作者: 徐雪松
作者简介:徐雪松（1970-），男，江西德兴人，副教授，博士，主要研究方向：多模型建模及控制；舒俭（1971-），男，江西南昌人，高级实验师，硕士，主要研究方向：计算机系统及仿真。
基金资助:
国家自然科学基金资助项目

Abstract

Abstract:

Classical regression algorithms for data set analysis of multiple models have the defects of long calculating time and low detecting accuracy of models. Therefore, a heuristic robust regression analysis method was proposed. This method mimicked the clustering principle of immune system. The B cell network was taken as classifier of data set and memory of model set. Conformity between data and model was used as the classification criteria, which improved the accuracy of the data classification. The extraction process of model set was divided into a parallel iterative trial including clustering, regressing and clustering again, by which the solution of model set was gradually approximated to. The simulation results show that the proposed algorithm needs obviously less calculating time and it has higher detecting accuracy of models than classical ones. According to the results of the eight-model data set analysis in this paper, among the classical algorithms, the best algorithm is the successive extraction algorithm based on Random Sample Consensus (RANSAC). Its mean model detecting accuracy is 90.37% and the calculating time is 53.3947s. The detecting accuracy of those classical algorithms which calculating time is below 0.5s is bellow 1%. By the contrary, the proposed algorithm needs only 0.5094s and its detecting accuracy is 98.25%.

摘要：

针对传统多模型数据集回归分析方法计算时间长、模型识别准确率低的问题，提出了一种新的启发式鲁棒回归分析方法。该方法模拟免疫系统聚类学习的原理，采用B细胞网络作为数据集的分类和存储工具，通过判断数据对模型的符合度进行分类，提高了数据分类的准确性，将模型集抽取过程分解成“聚类”“回归”“再聚类”的反复尝试过程，利用并行启发式搜索逼近模型集的解。仿真结果表明，所提方法回归分析时间明显少于传统算法，模型识别准确率明显高于传统算法。根据8模型数据集分析结果，传统算法中，效果最好的是基于RANSAC的逐次提取算法，其平均模型识别准确率为90.37%，需53.3947s；计算时间小于0.5s的传统算法，其准确率不足1%；所提算法仅需0.5094s，其准确率达到了98.25%。

CLC Number:

TP301.6

XU Xuesong SHU Jian. Immune robust regression analysis for data set of multiple models[J]. Journal of Computer Applications, 2014, 34(8): 2285-2290.

徐雪松舒俭. 多模型数据集的免疫鲁棒回归分析[J]. 计算机应用, 2014, 34(8): 2285-2290.

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Immune robust regression analysis for data set of multiple models

多模型数据集的免疫鲁棒回归分析

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