A Comparative Study on Learning to Rank with Computational Methods

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 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.

Suggestions

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.
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 ...
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...
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.