A Hybrid Computational Method based on Convex Optimizationfor Outlier Problems

2015-11-01
Yerlikaya Ozkurt, Fatma
Askan Gündoğan, Ayşegül
Weber, Gerhard Wiehelm
Statistical modeling plays a central role for any prediction problem of interest.However, predictive models may give misleading results when the data containoutliers. In many applications, it is important to identify and treat the outlierswithout direct elimination. To handle such issues, a hybrid computational methodbased on conic quadratic programming is introduced and employed onearthquake ground motion data set. Results are compared against widely-usedground motion prediction models.
The Institute for Operations Research and the Management Sciences (INFORMS) 2015 Annual Meeting

Suggestions

A Hybrid Computational Method Based on Convex Optimization for Outlier Problems: Application to Earthquake Ground Motion Prediction
Yerlikaya-Ozkurt, Fatma; Askan Gündoğan, Ayşegül; Weber, Gerhard-Wilhelm (Vilnius University Press, 2016-01-01)
Statistical modelling plays a central role for any prediction problem of interest. However, predictive models may give misleading results when the data contain outliers. In many real -world applications, it is important to identify and treat the outliers without direct elimination. To handle such issues, a hybrid computational method based on conic quadratic programming is introduced and employed on earthquake ground motion dataset. This method aims to minimize the impact of the outliers on regression estim...
A Hybrid Approach for Process Mining Using From to Chart Arranged by Genetic Algorithms LNCS San Sebastian Spain June 2010
Esgin, Eren; Karagöz, Pınar (2010-06-18)
In the scope of this study, a hybrid data analysis methodology to business process modeling is proposed in such a way that; From-to Chart, which is basically used as the front-end to figure out the observed patterns among the activities at realistic event logs, is rearranged by Genetic Algorithms to convert these derived raw relations into activity sequence. According to experimental results, acceptably good (sub-optimal or optimal) solutions are obtained for relatively complex business processes at a reaso...
An approach to the mean shift outlier model by Tikhonov regularization and conic programming
TAYLAN, PAKİZE; Yerlikaya-Oezkurt, Fatma; Weber, Gerhard Wilhelm (IOS Press, 2014-01-01)
In statistical research, regression models based on data play a central role; one of these models is the linear regression model. However, this model may give misleading results when data contain outliers. The outliers in linear regression can be resolved in two stages: by using the Mean Shift Outlier Model (MSOM) and by providing a new solution for this model. First, we construct a Tikhonov regularization problem for the MSOM. Then, we treat this problem using convex optimization techniques, specifically c...
A metamodeling methodology involving both qualitative and quantitative input factors
Tunali, S; Batmaz, I (Elsevier BV, 2003-10-16)
This paper suggests a methodology for developing a simulation metamodel involving both quantitative and qualitative factors. The methodology mainly deals with various strategic issues involved in metamodel estimation, analysis, comparison, and validation. To illustrate how to apply the methodology, a regression metamodel is developed for a client-server computer system. In particular, we studied how the response time is affected by the quantum interval, the buffer size. and the total number of terminals whe...
A matheuristic for binary classification of data sets using hyperboxes
Akbulut, Derya; İyigün, Cem; Özdemirel, Nur Evin (null; 2018-07-08)
In this study, an optimization approach is proposed for the binary classification problem. A Mixed Integer Programming (MIP) model formulation is used to construct hyperboxes as classifiers, minimizing the number of misclassified and unclassified samples as well as overlapping of hyperboxes. The hyperboxes are determined by some lower and upper bounds on the feature values, and overlapping of these hyperboxes is allowed to keep a balance between misclassification and overfitting. A matheuristic, namely Iter...
Citation Formats
F. Yerlikaya Ozkurt, A. Askan Gündoğan, and G. W. Weber, “A Hybrid Computational Method based on Convex Optimizationfor Outlier Problems,” presented at the The Institute for Operations Research and the Management Sciences (INFORMS) 2015 Annual Meeting, 2015, Accessed: 00, 2021. [Online]. Available: https://cld.bz/KAj90ao#466.