图 书 馆
roc空间中的子群发现与规则求值Subgroup discovery and rule evaluation in ROC space |
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| 课程网址: | http://videolectures.net/solomon_lavrac_sdrer/ |
| 主讲教师: | Peter A. Flach, Nada Lavrač |
| 开课单位: | 布里斯托尔大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Traditionally, machine learning has focussed on induction of classification and prediction rules. More recently, non-predictive or descriptive induction is gaining substantial interest of machine learning researchers. Two major trends in descriptive induction are association rule learning and subgroup discovery. In this seminar we present our recent work in descriptive induction. We also argue that accuracy is not always an appropriate evaluation measure in the descriptive induction framework, and propose quality measures designed for subgroup evaluation in ROC space. After a brief presentation of the APRIORI-C and SD-algorithm, we give a detailed presentation of the CN2-SD algorithm, which includes a new -- weighted -- covering algorithm, a new search heuristic (weighted relative accuracy), probabilistic classification of instances, and a new measure for evaluating the results of subgroup discovery (area under ROC curve). The presented work was done in collaboration with V. Jovanoski (APRIORI-C), D. Gamberger (SD-algorithm), B. Kavsek and L. Todorovski (CN2-SD algorithm). Our research was supported by the Slovenian Ministry of Education, Science and Sport, the IST-1999-11495 project Data Mining and Decision Support for Business Competitiveness: A European Virtual Enterprise, and the British Council project Partnership in Science PSP-18. |
| 关 键 词: | 机器学习; 描述性归纳; 加权覆盖算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-22：cwx |
| 阅读次数: | 52 |