A Comparative Study on Learning to Rank with Computational Methods

Date

2017-12-14

Author

Batmaz, İnci
Karagöz, Pınar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

148
views

0
downloads

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 approach and compare the performances of four computational methods in developing ranking models using several criteria such as accuracy, stability and robustness. The experimental results show that Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Networks (ANN) are effective methods for learning to rank problem and provide promising results.

Subject Keywords

Web search query, Artificial neural networks (ANN), Support vector machines (SVM), Multivariate adaptive regression spline (MARS), Conic multivariate adaptive regression splines (CMARS)

URI

https://hdl.handle.net/11511/54809

Collections

Department of Statistics, Conference / Seminar

Suggestions

OpenMETU
Core

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 focus on point-wise learning to rank, where the model learns the ranking values. Multivariate adaptive regression splines (MARS) and conic multivariate adaptive regression splines (CMARS) are supervised learning techniques that have been proven to provide successful results on various predi...
An experimental comparison of symbolic and neural learning algorithms Baykal, Nazife (1998-04-23) In this paper comparative strengths and weaknesses of symbolic and neural learning algorithms are analysed. Experiments comparing the new generation symbolic algorithms and neural network algorithms have been performed using twelve large, real-world data sets.
Learning to rank web data using multivariate adaptive regression splines Altınok, Gülşah; Batmaz, İnci; Karagöz, Pınar; Department of Statistics (2018) A new trend, called learning to rank, has recently come to light in a wide variety of applications in Information Retrieval (IR), Natural Language Processing (NLP), and Data Mining (DM), to utilize machine learning techniques to automatically build the ranking models. Typical applications are document retrieval, expert search, definition search, collaborative filtering, question answering, and machine translation. In IR, there are three approaches used for ranking. The one is traditional model approaches su...
A pattern classification approach for boosting with genetic algorithms Yalabık, Ismet; Yarman Vural, Fatoş Tunay; Üçoluk, Göktürk; Şehitoğlu, Onur Tolga (2007-11-09) Ensemble learning is a multiple-classifier machine learning approach which produces collections and ensembles statistical classifiers to build up more accurate classifier than the individual classifiers. Bagging, boosting and voting methods are the basic examples of ensemble learning. In this study, a novel boosting technique targeting to solve partial problems of AdaBoost, a well-known boosting algorithm, is proposed. The proposed system finds an elegant way of boosting a bunch of classifiers successively ...
Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance Koc, Elcin Kartal; İyigün, Cem; Batmaz, İnci; Weber, Gerhard-Wilhelm (2014-09-01) Multivariate adaptive regression splines (MARS) has become a popular data mining (DM) tool due to its flexible model building strategy for high dimensional data. Compared to well-known others, it performs better in many areas such as finance, informatics, technology and science. Many studies have been conducted on improving its performance. For this purpose, an alternative backward stepwise algorithm is proposed through Conic-MARS (CMARS) method which uses a penalized residual sum of squares for MARS as a T...

Citation Formats

İ. Batmaz and P. Karagöz, “A Comparative Study on Learning to Rank with Computational Methods,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54809.