Multiple Ordinal Regression by Maximizing the Sum of Margins
| Autor: | Aleix M. Martinez, Onur C. Hamsici |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Ordinal data
Mathematical optimization Class (set theory) Support Vector Machine Computer Networks and Communications Decision Making 02 engineering and technology Machine learning computer.software_genre Ordinal regression Article Artificial Intelligence Margin (machine learning) 020204 information systems 0202 electrical engineering electronic engineering information engineering Ordinal data type Humans Learning Mathematics business.industry Computer Science Applications Ordinal optimization Ranking Sequential minimal optimization 020201 artificial intelligence & image processing Artificial intelligence Neural Networks Computer business computer Software Algorithms |
| Popis: | Human preferences are usually measured using ordinal variables. A system whose goal is to estimate the preferences of humans and their underlying decision mechanisms requires to learn the ordering of any given sample set. We consider the solution of this ordinal regression problem using a Support Vector Machine algorithm. Specifically, the goal is to learn a set of classifiers with common direction vectors and different biases correctly separating the ordered classes. Current algorithms are either required to solve a quadratic optimization problem, which is computationally expensive, or are based on maximizing the minimum margin (i.e., a fixed margin strategy) between a set of hyperplanes, which biases the solution to the closest margin. Another drawback of these strategies is that they are limited to order the classes using a single ranking variable (e.g., perceived length). In this paper, we define a multiple ordinal regression algorithm based on maximizing the sum of the margins between every consecutive class with respect to one or more rankings (e.g., perceived length and weight). We provide derivations of an efficient, easy-to-implement iterative solution using a Sequential Minimal Optimization procedure. We demonstrate the accuracy of our solutions in several datasets. In addition, we provide a key application of our algorithms in estimating human subjects’ ordinal classification of attribute associations to object categories. We show that these ordinal associations perform better than the binary one typically employed in the literature. |
| Databáze: | OpenAIRE |
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