Genetic Programming for Attribute Construction in Data Mining

Otero, Fernando E. B.; Silva, Monique M. S.; Freitas, Alex A.; Nievola, Júlio César

doi:10.1007/3-540-36599-0_36

Cited by 73 publications

(55 citation statements)

References 7 publications

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“…It is well known that the classification performance is dependent of the quality of the data space representation (i.e., attributes of the data) [19], [20]. If the attributes defining the data space-the set of indicators in the case of EDDIE-are inadequate, it becomes more difficult to create GDTs with a high predictive quality.…”

Section: Incorporating Attribute Construction In Eddiementioning

confidence: 99%

“…Hu [20] proposed a GP for attribute construction called GPCI following the filter strategy. The terminal set of the GP consists of the booleanized original attributes and the function set consists of the AND and OR operators; each individual represent a candidate new attribute, created by combining the (now boolean) attributes using the AND and OR operators.…”

Section: Incorporating Attribute Construction In Eddiementioning

confidence: 99%

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Using Attribute Construction to Improve the Predictability of a GP Financial Forecasting Algorithm

Kampouridis

Otero

2013

2013 Conference on Technologies and Applications of Artificial Intelligence

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Abstract-Financial forecasting is an important area in computational finance. EDDIE 8 is an established Genetic Programming financial forecasting algorithm, which has successfully been applied to a number of international datasets. The purpose of this paper is to further increase the algorithm's predictive performance, by improving its data space representation. In order to achieve this, we use attribute construction to create new (high-level) attributes from the original (low-level) attributes. To examine the effectiveness of the above method, we test the extended EDDIE's predictive performance across 25 datasets and compare it to the performance of two previous EDDIE algorithms. Results show that the introduction of attribute construction benefits the algorithm, allowing EDDIE to explore the use of new attributes to improve its predictive accuracy.

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Section: Incorporating Attribute Construction In Eddiementioning

confidence: 99%

Section: Incorporating Attribute Construction In Eddiementioning

confidence: 99%

Using Attribute Construction to Improve the Predictability of a GP Financial Forecasting Algorithm

Kampouridis

Otero

2013

2013 Conference on Technologies and Applications of Artificial Intelligence

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“…Examples of this approach are the GP algorithms for attribute construction proposed by (Otero et al 2003;Hu 1998), whose attribute evaluation function (the fitness function) is the information gain ratio -a measure discussed in detail in (Quinlan 1993). In addition, (Muharram & Smith 2004) did experiments comparing the effectiveness of two different attributeevaluation criteria in GP for attribute construction -viz.…”

Section: Data Preprocessing Vs Interleaving Approachmentioning

confidence: 99%

A Review of Evolutionary Algorithms for Data Mining

Freitas

2009

Data Mining and Knowledge Discovery Handbook

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Summary. Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that they are robust, adaptive search techniques that perform a global search in the solution space. This chapter first presents a brief overview of EAs, focusing mainly on two kinds of EAs, viz. Genetic Algorithms (GAs) and Genetic Programming (GP). Then the chapter reviews the main concepts and principles used by EAs designed for solving several data mining tasks, namely: discovery of classification rules, clustering, attribute selection and attribute construction. Finally, it discusses Multi-Objective EAs, based on the concept of Pareto dominance, and their use in several data mining tasks.

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“…In [17], the authors use typed GP for building feature extractors. Functions are arithmetic and relational operators.…”

Section: Related Workmentioning

confidence: 99%

GPPE: a method to generate ad-hoc feature extractors for prediction in financial domains

2007

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When dealing with classificatio and regression problems, there is a strong need for high-quality attributes. This is a capital issue not only in financia problems, but in many Data Mining domains. Constructive Induction methods help to overcome this problem by mapping the original representation into a new one, where prediction becomes easier. In this work we present GPPE: a GP-based method that projects data from an original data space into another one where data approaches linear behavior (linear separability or linear regression). Also, GPPE is able to reduce the dimensionality of the problem by recombining related attributes and discarding irrelevant ones. We have applied GPPE to two financia domains: Bankruptcy prediction and IPO Underpricing prediction. In both cases GPPE automatically generated a new data representation that obtained competitive prediction rates and drastically reduced the dimensionality of the problem.

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Genetic Programming for Attribute Construction in Data Mining

Cited by 73 publications

References 7 publications

Using Attribute Construction to Improve the Predictability of a GP Financial Forecasting Algorithm

Using Attribute Construction to Improve the Predictability of a GP Financial Forecasting Algorithm

A Review of Evolutionary Algorithms for Data Mining

GPPE: a method to generate ad-hoc feature extractors for prediction in financial domains

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