Liedtke C E, Greenberg W, Tuna N
Int J Biomed Comput. 1977 Jan;8(1):35-44. doi: 10.1016/0020-7101(77)90054-x.
This paper describes the influence of different populations on statistical multivariate classification rules and classification results where the word "population" refers only to the frequency of diagnoses to be expected, the so-called prior probabilities. Using linear regression as a multivariate classification technique and six groups consisting of five pathological conditions and normals as test data, it has been shown: (a) That the population influences to a great extent the selection of the best ECG-VCG measurements for the classification rule. (b) That a mismatch of the populations in the learning and test sets can considerably decrease the number of correct classifications. (c) That a certain correction of the mismatch can be achieved when the prior probabilities in the learning and test sets are known, Further, the paper discusses the change of prior probabilities over the years at the Variety Club Heart Hospital in the University of Minnesota and its effect on the performance of the classification algorithm which has been used.
本文描述了不同总体对统计多变量分类规则和分类结果的影响，其中“总体”一词仅指预期诊断的频率，即所谓的先验概率。使用线性回归作为多变量分类技术，并以由五种病理状况和正常情况组成的六组作为测试数据，结果表明：(a)总体在很大程度上影响分类规则中最佳心电图-向量心电图测量值的选择。(b)学习集和测试集中总体的不匹配会显著减少正确分类的数量。(c)当学习集和测试集中的先验概率已知时，可以对不匹配进行一定的校正。此外，本文还讨论了明尼苏达大学综艺俱乐部心脏医院多年来先验概率的变化及其对所使用分类算法性能的影响。
