A Comparison between Two Classification Algorithms by Implementing Bias-variance Decomposition
We experimentally compare the performance of Multiple Criteria Linear Programming（MCLP) and Linear Discriminant Analysis(LDA)classification algorithms by implementing bias-variance decomposition. Under Domingos” bias-variance decomposition framework，by using bagging ensemble，we compared their bias，variance and their variations with the size of training set on three data sets. We aimed to comparing their classification accuracy，diversity and other main characteristics. The experimental results show that，MCLP and LDA are all simple and effective classification algorithms. When training set is large enough，they present almost the same good performance.But they still behave differently in some aspects. LDA is more stable than MCLP while MCLP is more suitable for large training sets. IN their own bias-variance structures，LDA presents high bias and low variance while MCLP has the oppositional characteristics to LDA.
多准则线性规划 线性判别分析 方差分解 分类算法
Meihong Zhu Aihua Li
Research Center on Fictitious Economy & Data Sciences, Chinese Academy of Sciences, 100080 School of School of Management Science and Engineering, Central University of Finance and Economics, 100081
国内会议
北京
英文
235-240
2008-11-11（万方平台首次上网日期，不代表论文的发表时间）