Back to Search

Multi-instance learning by maximizing the area under receiver operating characteristic curve

Title	Multi-instance learning by maximizing the area under receiver operating characteristic curve
Author	Sakarya, I. E., Kundakcıoğlu, Ömer Erhun
Publication Date:	2023-02
Publication Place	- Springer
Subject	Area under curve, Mixed integer linear programming, Multi-instance learning
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	0925-5001
Record ID	ad0fc349-2d1b-428b-8d28-803adafb7906
Library Location	Industrial Engineering
Date	2023-02
Sample Text	The purpose of this study is to solve the multi-instance classification problem by maximizing the area under the Receiver Operating Characteristic (ROC) curve obtained for witness instances. We derive a mixed integer linear programming model that chooses witnesses and produces the best possible ROC curve using a linear ranking function for multi-instance classification. The formulation is solved using a commercial mathematical optimization solver as well as a fast metaheuristic approach. When the data is not linearly separable, we illustrate how new features can be generated to tackle the problem. We present a comprehensive computational study to compare our methods against the state-of-the-art approaches in the literature. Our study reveals the success of an optimal linear ranking function through cross validation for several benchmark instances.
DOI	10.1007/s10898-022-01219-y
Cilt	85

View in source Özyeğin University Özyeğin University - Historical works, archives, and periodicals search engine

Özyeğin University

Multi-instance learning by maximizing the area under receiver operating characteristic curve

Author Sakarya, I. E., Kundakcıoğlu, Ömer Erhun

Publication Date 2023-02

Publication Place - Springer

Subject Area under curve, Mixed integer linear programming, Multi-instance learning

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 0925-5001

Record ID ad0fc349-2d1b-428b-8d28-803adafb7906

Library Location Industrial Engineering

Date 2023-02

Sample Text The purpose of this study is to solve the multi-instance classification problem by maximizing the area under the Receiver Operating Characteristic (ROC) curve obtained for witness instances. We derive a mixed integer linear programming model that chooses witnesses and produces the best possible ROC curve using a linear ranking function for multi-instance classification. The formulation is solved using a commercial mathematical optimization solver as well as a fast metaheuristic approach. When the data is not linearly separable, we illustrate how new features can be generated to tackle the problem. We present a comprehensive computational study to compare our methods against the state-of-the-art approaches in the literature. Our study reveals the success of an optimal linear ranking function through cross validation for several benchmark instances.

DOI 10.1007/s10898-022-01219-y

Cilt 85