Learning to assess from pair-wise comparisons

In this paper we present an algorithm for learning a function able to assess objects. We assume that our teachers can provide a collection of pairwise comparisons but encounter certain difficulties in assigning a number to the qualities of the objects considered. This is a typical situation when dealing with food products, where it is very interesting to have repeatable, reliable mechanisms that are as objective as possible to evaluate quality in order to provide markets with products of a uniform quality. The same problem arises when we are trying to learn user preferences in an information retrieval system or in configuring a complex device. The algorithm is implemented using a growing variant of Kohonen’s Self-Organizing Maps (growing neural gas), and is tested with a variety of data sets to demonstrate the capabilities of our approac

The use of machine learning algorithms for the study of business profitability : a new approach based on preferences

In recent years, researchers in the Field of Artificial Intelligence have developed a learning technique, namely, preference learning, that is suitable to be used for economic analysis. The present research empirically tests one of these models, which consists of a combination of LACE and RFE algorithms. The problem of forecasting the profitability of Spanish companies upon the basis of a set of financial ratios is used as a benchmark. The model provides forecasted rankings, which are a kind of information that is more useful for the economic analysts than the forecasted class memberships that traditional machine learning techniques provide.En los últimos años, investigadores del campo de la Inteligencia Artificial han desarrollado una técnica de aprendizaje llamada “aprendizaje de referencia”, que puede usarse para el análisis económico. Esta investigación pone a prueba de manera empírica uno de estos modelos, que consiste en la combinación de algoritmos LACE y RFE. El problema de investigar los posibles beneficios de compañías españolas por encima de la base de un set de ratios financieros se usa como limitación. El modelo proporciona rankings pronosticados, los cuales son un tipo de información que es más útil para los analistas económicos que para los miembros que las técnicas que el aprendizaje tradicional proporciona

Learning to Assess From Pair-Wise Comparisons

In this paper we present an algorithm for learning a function able to assess objects. We assume that our teachers can provide a collection of pairwise comparisons but encounter certain difficulties in assigning a number to the qualities of the objects considered. This is a typical situation when dealing with food products, where it is very interesting to have repeatable, reliable mechanisms that are as objective as possible to evaluate quality in order to provide markets with products of a uniform quality. The same problem arises when we are trying to learn user preferences in an information retrieval system or in configuring a complex device. The algorithm is implemented using a growing variant of Kohonen's Self-Organizing Maps (growing neural gas), and is tested with a variety of data sets to demonstrate the capabilities of our approach