[1]蒲凌杰,曾繁慧,汪培庄.因素空间理论下基点分类算法研究[J].智能系统学报,2020,15(3):528-536.[doi:10.11992/tis.201903031]
PU Lingjie,ZENG Fanhui,WANG Peizhuang.Base point classification algorithm based on factor space theory[J].CAAI Transactions on Intelligent Systems,2020,15(3):528-536.[doi:10.11992/tis.201903031]
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PU Lingjie,ZENG Fanhui,WANG Peizhuang.Base point classification algorithm based on factor space theory[J].CAAI Transactions on Intelligent Systems,2020,15(3):528-536.[doi:10.11992/tis.201903031]
因素空间理论下基点分类算法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第3期
页码:
528-536
栏目:
学术论文—人工智能基础
出版日期:
2020-05-05
- Title:
- Base point classification algorithm based on factor space theory
- Keywords:
- factor space; background base; background base extraction; λ-background base; base point classification algorithm; identify new classes; data classification; background distribution; background relationship
- 分类号:
- TP18
- 摘要:
- 目前，基于因素空间理论的背景基提取算法计算过程复杂，初始化必须依赖各因素极值，基点数量提取冗余等原因，未能在应用中取得很好效果。为此，结合内点判别法和知识可继承、可扩展的思想，提出一种计算简单、初始化独立、基点数量小的改进的背景基提取算法。然后，利用改进的背景基提取算法构造出一种全新的数据分类算法-基点分类算法，基点分类算法以提取每一类样本的背景基为预测模型，再通过新定义的λ-背景基，优化预测模型。数值实验表明：基点分类算法原理简单、构造难度小、分类模型泛化能力强，预测能力准确率高，同时严格的模型限定区域又能为识别新类别提供新方法。
- Abstract:
- At present, the background-based extraction algorithm based on factor space theory has not achieved good results when used in applications. Reasons for its inefficiency are the calculation process is complicated, initialization depends on the extreme values of each factor, and redundancy of the number of base points extracted. Therefore, combining the inner point judgment method and a novel idea, an improved background-based extraction algorithm with simple calculation, independent initialization, and a few number of base points is proposed. Using the improved background-based extraction algorithm, a new data classification algorithm, i.e., base point classification algorithm, is constructed. The algorithm extracts the background base of each type of sample as the prediction model and optimizes the prediction model through the newly defined λ-background base. Finally, numerical experiments show that the base point classification algorithm is simple in principle, easy in construction, strong in generalizing the ability of classification model, and high in accuracy of prediction ability. Moreover, strict-utility model can provide new methods for identifying new classes.
- 参考文献/References:
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备注/Memo
收稿日期:2019-03-23。
基金项目:国家自然科学基金委主任基金项目(61350003)；辽宁省教育厅科学技术研究经费项目(LJ2019JL019)
作者简介:蒲凌杰，硕士研究生，主要研究方向为因素空间理论、数据挖掘、智能决策;曾繁慧，教授，主要研究方向为基于因素空间的数据挖掘理论与应用、模糊结构元理论与应用。参与完成中国工程院重点项目、国家自然科学基金项目、辽宁省基金项目、教育部高校博士学科点专项科研基金项目等。获多项省、市级奖励。发表学术论文 50余篇; 汪培庄，教授，博士生导师，主要研究方向为模糊数学及其在人工智能中的应用，近期主要致力于因素空间在人工智能和数据科学中的应用研究。提出和创立了模糊集的随机落影表示、真值流推理和因素空间等数学理论，多次获得国家级和部委级奖励， 获得一次国际奖。发表学术论文200余篇，出版学术著作4部。
通讯作者:蒲凌杰.E-mail:1901676469@qq.com
更新日期/Last Update:
1900-01-01