Explanation and reliability of prediction models: the case of breast cancer recurrence

被引：28

作者：

Strumbelj, Erik ^{[1
]}

Bosnic, Zoran ^{[1
]}

Kononenko, Igor ^{[1
]}

Zakotnik, Branko ^{[2
]}

Kuhar, Cvetka Grasic ^{[2
]}

机构：

[1] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia

[2] Inst Oncol, Ljubljana, Slovenia

来源：

KNOWLEDGE AND INFORMATION SYSTEMS | 2010年 / 24卷 / 02期

关键词：

Data mining; Machine learning; Breast cancer; Classification explanation; Prediction reliability; ALGORITHMS; SELECTION;

D O I：

10.1007/s10115-009-0244-9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers' predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation methodology for regression predictions, which helps the users to decide to what extent to trust a particular prediction. Both methods are successfully applied to a novel breast cancer recurrence prediction data set and the results are evaluated by expert oncologists.

引用

页码：305 / 324

页数：20

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