Relational differential prediction

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Nassif, H (Author) Other; Santos Costa, V (Author) FCUP; Burnside, ES (Author) Other; Page, D (Author) Other

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 617-632

2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012

Bristol, 24 September 2012 through 28 September 2012

Indexing

Scopus - 8 Citations

Other information

Authenticus ID: P-008-6D6

DOI: 10.1007/978-3-642-33460-3_45

Abstract (EN): A typical classification problem involves building a model to correctly segregate instances of two or more classes. Such a model exhibits differential prediction with respect to given data subsets when its performance is significantly different over these subsets. Driven by a mammography application, we aim at learning rules that predict breast cancer stage while maximizing differential prediction over age-stratified data. In this work, we present the first multi-relational differential prediction (aka uplift modeling) system, and propose three different approaches to learn differential predictive rules within the Inductive Logic Programming framework. We first test and validate our methods on synthetic data, then apply them on a mammography dataset for breast cancer stage differential prediction rule discovery. We mine a novel rule linking calcification to in situ breast cancer in older women. © 2012 Springer-Verlag.

Language: English

Type (Professor's evaluation): Scientific