Cost matters: A new example-dependent cost-sensitive logistic regression model

Nikou Günnemann, Jürgen Pfeffer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


Connectivity and automation are evermore part of today’s cars. To provide automation, many gauges are integrated in cars to collect physical readings. In the automobile industry, the gathered multiple datasets can be used to predict whether a car repair is needed soon. This information gives drivers and retailers helpful information to take action early. However, prediction in real use cases shows new challenges: misclassified instances have not equal but different costs. For example, incurred costs for not predicting a necessarily needed tire change are usually higher than predicting a tire change even though the car could still drive thousands of kilometers. To tackle this problem, we introduce a new example-dependent cost sensitive prediction model extending the well-established idea of logistic regression. Our model allows different costs of misclassified instances and obtains prediction results leading to overall less cost. Our method consistently outperforms the state-of-the-art in example-dependent cost-sensitive logistic regression on various datasets. Applying our methods to vehicle data from a large European car manufacturer, we show cost savings of about 10%.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
EditorsKyuseok Shim, Jae-Gil Lee, Longbing Cao, Xuemin Lin, Jinho Kim, Yang-Sae Moon
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319574530
StatePublished - 2017
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
Duration: 23 May 201726 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10234 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
Country/TerritoryKorea, Republic of


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