Maximizing benefit of classifications using feature intervals

被引：0

作者：

Ikizler, N ^{[1
]}

Güvenir, HA ^{[1
]}

机构：

[1] Bilkent Univ, Dept Comp Engn, TR-06533 Ankara, Turkey

来源：

KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS | 2003年 / 2773卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

There is a great need for classification methods that can properly handle asymmetric cost and benefit constraints of classifications. In this study, we aim to emphasize the importance of classification benefits by means of a new classification algorithm, Benefit-Maximizing classifier with Feature Intervals (BMFI) that uses feature projection based knowledge representation. Empirical results show that BMFI has promising performance compared to recent cost-sensitive algorithms in terms of the benefit gained.

引用

页码：339 / 345

页数：7

共 8 条

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