Learning to rank web data using multivariate adaptive regression splines

Download

index.pdf

Date

2018

Author

Altınok, Gülşah

Metadata

Show full item record

Item Usage Stats

266
views

106
downloads

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 such as Boolean Model (BM), Vector Space Model (VSM) and classical Probabilistic Model (classical PM). The second approach is called Language Model (LM). Such models are n-gram Model, Query Likelihood Model (QLM). The final method is namely system model including Support Vector Model (SVM) and Artificial Neural Network (ANN). In this study, we adopted the system model approach and compared the performance measures of those widely used models, SVM and ANN with those Multivariate Adaptive Regression Splines (MARS) and its variant Conic Multivariate Adaptive Regression Splines (CMARS). Results indicate that MARS performs slightly better than the others considered in this study

Subject Keywords

Information retrieval., Multivariate analysis., Regression analysis., Machine learning., Data mining.

URI

http://etd.lib.metu.edu.tr/upload/12622807/index.pdf
https://hdl.handle.net/11511/27833

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Multiobjective evolutionary feature subset selection algorithm for binary classification Deniz Kızılöz, Firdevsi Ayça; Coşar, Ahmet; Dökeroğlu, Tansel; Department of Computer Engineering (2016) This thesis investigates the performance of multiobjective feature subset selection (FSS) algorithms combined with the state-of-the-art machine learning techniques for binary classification problem. Recent studies try to improve the accuracy of classification by including all of the features in the dataset, neglecting to determine the best performing subset of features. However, for some problems, the number of features may reach thousands, which will cause too much computation power to be consumed during t...
MODELLING OF KERNEL MACHINES BY INFINITE AND SEMI-INFINITE PROGRAMMING Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03) In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
Using data analytics for collaboration patterns in distributed software team simulations Dafoulas, Georgios A.; Serce, Fatma C.; SWİGGER, Kathleen; BRAZİLE, Robert; Alpaslan, Ferda Nur; Alpaslan, Ferda Nur; Milewski, Allen (2016-08-05) This paper discusses how previous work on global software development learning teams is extended with the introduction of data analytics. The work is based on several years of studying student teams working in distributed software team simulations. The scope of this paper is twofold. First it demonstrates how data analytics can be used for the analysis of collaboration between members of distributed software teams. Second it describes the development of a dashboard to be used for the visualization of variou...
Data mining analysis of economic indicators of countries Güngör, Erdem; Yozgatlıgil, Ceylan; Department of Statistics (2020-8) Data Mining is becoming a famous analysis day by day to reveal the hidden information within big data. In the study, we use data mining techniques on the economic indicators of the countries. The four data mining techniques are to be implemented on the dataset. Making homogenous groups of the countries whose economic characteristics are similar are obtained by the Clustering Algorithm. After the clustering algorithm is performed, we pass to Association Rule Data Mining to investigate the most exported produ...
Using Active Learning to Allow Activity Recognition on a Large Scale Alemdar, Hande; ERSOY, CEM (2011-11-18) Automated activity recognition systems that use probabilistic models require labeled data sets in training phase for learning the model parameters. The parameters are different for every person and every environment. Therefore, for every person or environment, training is needed to be performed from scratch. Obtaining labeled data requires much effort therefore poses challenges on the large scale deployment of activity recognition systems. Active learning can be a solution to this problem. It is a machine l...

Citation Formats

G. Altınok, “Learning to rank web data using multivariate adaptive regression splines,” M.S. - Master of Science, Middle East Technical University, 2018.