On Equivalence Relationships Between Classification and Ranking Algorithms

Date

2011-10-01

Author

Ertekin Bolelli, Şeyda

Metadata

Show full item record

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

Item Usage Stats

163
views

0
downloads

We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from solving one task can be carried over to the other task, such as the ability to obtain conditional density estimates, and an order-of-magnitude reduction in computational time for training the algorithm. It also means that some algorithms are robust to the choice of evaluation metric used; they can theoretically perform well when performance is measured either by a misclassification error or by a statistic of the ROC curve (such as the area under the curve). Specifically, we provide such an equivalence relationship between a generalization of Freund et al.'s RankBoost algorithm, called the "P-Norm Push," and a particular cost-sensitive classification algorithm that generalizes AdaBoost, which we call "P-Classification." We discuss and validate the potential benefits of this equivalence relationship, and perform controlled experiments to understand P-Classification's empirical performance. There is no established equivalence relationship for logistic regression and its ranking counterpart, so we introduce a logistic-regression-style algorithm that aims in between classification and ranking, and has promising experimental performance with respect to both tasks.

Subject Keywords

Logistic regression, Boosting, Rank statistics, Area under the curve, Bipartite ranking, Supervised classification

URI

https://hdl.handle.net/11511/53499

Journal

JOURNAL OF MACHINE LEARNING RESEARCH

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

On numerical optimization theory of infinite kernel learning Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2010-10-01) In Machine Learning algorithms, one of the crucial issues is the representation of the data. As the given data source become heterogeneous and the data are large-scale, multiple kernel methods help to classify "nonlinear data". Nevertheless, the finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, a novel method of "infinite" kernel combinations is proposed with the help of infinite and semi-infinite programming regarding all elements in kernel space. Look...
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...
Consensus clustering of time series data Yetere Kurşun, Ayça; Batmaz, İnci; İyigün, Cem; Department of Scientific Computing (2014) In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time serie...
Machine Learning over Encrypted Data With Fully Homomorphic Encyption Kahya, Ayşegül; Cenk, Murat; Department of Cryptography (2022-8-26) When machine learning algorithms train on a large data set, the result will be more realistic. Big data, distribution of big data, and the study of learning algorithms on distributed data are popular research topics of today. Encryption is a basic need, especially when storing data with a high degree of confidentiality, such as medical data. Classical encryption methods cannot meet this need because when texts encrypted with classical encryption methods are distributed, and the distributed data set is decry...
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.

Citation Formats

Ş. Ertekin Bolelli, “On Equivalence Relationships Between Classification and Ranking Algorithms,” JOURNAL OF MACHINE LEARNING RESEARCH, pp. 2905–2929, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53499.