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

Name: Multi-instance learning by maximizing the area under receiver operating characteristic curve
Author: Sakarya, I. E., Kundakcıoğlu, Ömer Erhun

İsim	Multi-instance learning by maximizing the area under receiver operating characteristic curve
Yazar	Sakarya, I. E., Kundakcıoğlu, Ömer Erhun
Basım Tarihi:	2023-02
Basım Yeri	- Springer
Konu	Area under curve, Mixed integer linear programming, Multi-instance learning
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0925-5001
Kayıt Numarası	ad0fc349-2d1b-428b-8d28-803adafb7906
Lokasyon	Industrial Engineering
Tarih	2023-02
Örnek Metin	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

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

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

Basım Tarihi 2023-02

Basım Yeri - Springer

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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0925-5001

Kayıt Numarası ad0fc349-2d1b-428b-8d28-803adafb7906

Lokasyon Industrial Engineering

Tarih 2023-02

Örnek Metin 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