İnci Batmaz

E-mail

ibatmaz@metu.edu.tr

Department

Department of Statistics

ORCID

0000-0002-0821-3786

Scopus Author ID

6506670261

Web of Science Researcher ID

B-1614-2018

cmaRs: A powerful predictive data mining package in R Yerlikaya-Özkurt, Fatma; Yazıcı, Ceyda; Batmaz, İnci (2023-12-01) Conic Multivariate Adaptive Regression Splines (CMARS) is a very successful method for modeling nonlinear structures in high-dimensional data. It is based on MARS algorithm and utilizes Tikhonov regularization and Conic Qu...
Data Mining Approaches to Meteorological Data: A Review of NINLIL Climate Research Group Studies Batmaz, İnci (CRC, New York , 2023-12-01)
Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines Altinok, Gulsah; Karagöz, Pınar; Batmaz, İnci (Wiley, 2020-10-22) Learning to rank is a supervised learning problem that aims to construct a ranking model for the given data. The most common application of learning to rank is to rank a set of documents against a query. In this work, we f...
StreamMARS: A Streaming Multivariate Adaptive Regression Splines Algorithm Batmaz, İnci (2019-12-14) Computers and internet have become inevitable parts of our life in the 1990s, and afterwards, bulk of data are started being recorded in digital platforms automatically. To extract meaningful patterns from such data comput...
Input variable selection for hydrological predictions in ungauged catchments: with or without clustering Doğulu, Nilay; Batmaz, İnci; Kentel Erdoğan, Elçin (null; 2018-04-08) A key step in data-driven environmental modelling, including for hydrological purposes, is input variable selection (IVS) to ensure that the least number of variables with minimum redundancy are used to characterize the in...
Clustering approaches for analysing similarity in ungagued catchments: input variable selection for hydrological predictions Doğulu, Nilay; Batmaz, İnci; Kentel Erdoğan, Elçin (2018-04-08) Catchments are hydrological units that exhibit unique but distinct features that greatly contribute to heterogeneity and complexity of rainfall-runoff processes. While the lure of understanding such diversity has underpinn...
A Categorical Principal Component Regression on Computer-Assisted Instruction in Probability Domain Kapucu, Tuğba; İlk Dağ, Özlem; Batmaz, İnci (2018-01-01) The purpose of this study is to examine the effects of computer-assisted instructional material (CAIM) prepared in R program on eighth grade students’ permutation–combination and probability achievement and their attitudes...
A Comparative Study on Learning to Rank with Computational Methods Batmaz, İnci; Karagöz, Pınar (2017-12-14) Learning to rank is a supervised learning problem that aims to construct a ranking model. The most common application of learning to rank is to rank a set of documents against a query. In this work, we focus on pointwise a...
A data mining application to deposit pricing: Main determinants and prediction models Batmaz, İnci; Danışoğlu, Seza (2017-11-01) This study provides unique empirical evidence regarding the determinants of deposit pricing by employing data mining methods and making use of proprietary data provided by a commercial bank. Results highlight the importanc...
Application of bootstrap to likelihood ratio test to detect multiple changepoints in small time series data. Yazıcı, Ceyda; Yozgatlıgil, Ceylan; Batmaz, İnci (2017-07-21)

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