Learning convolutional neural network to maximize Pos@Top performance measure

Yanyan Geng, Ru-Ze Liang, Weizhi Li, Jingbin Wang, Gaoyuan Liang, Chenhao Xu, Jing Yan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations


In the machine learning problems, the performance measure is used to evaluate the machine learning models. Recently, the number positive data points ranked at the top positions (Pos@Top) has been a popular performance measure in the machine learning community. In this paper, we propose to learn a convolutional neural network (CNN) model to maximize the Pos@Top performance measure. The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation. We propose to minimize the loss function of Pos@Top over a training set to learn the filters of CNN and the classifier parameter. The classifier parameter vector is solved by the Lagrange multiplier method, and the filters are updated by the gradient descent method alternately in an iterative algorithm. Experiments over benchmark data sets show that the proposed method outperforms the state-of-the-art Pos@Top maximization methods.
Original languageEnglish (US)
Title of host publication25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017
Publisheri6doc.com publication
Number of pages6
ISBN (Print)9782875870391
StatePublished - Jan 1 2017

Bibliographical note

KAUST Repository Item: Exported on 2021-04-16


Dive into the research topics of 'Learning convolutional neural network to maximize Pos@Top performance measure'. Together they form a unique fingerprint.

Cite this