Miele, Raffaele (2006) Nature inspired Optimization Algorithms for Classification and Regression Trees. [Tesi di dottorato] (Unpublished)

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Item Type: Tesi di dottorato
Uncontrolled Keywords: Classification and Regression Trees, Genetic Algorithms, Ant Colony Optimization
Date Deposited: 30 Jul 2008
Last Modified: 30 Apr 2014 19:23
URI: http://www.fedoa.unina.it/id/eprint/609

Abstract

During the past 10 years Computational Statistics techniques' importance has been always increasing and it is actually still doing. This is due to more than one factor. On one side, the technological growth of the last decades has made computational power and storage capacity incredibly affordable and accessible. In particular, the storage capacity has improved much more than the computational resources. This resulted in an unbalanced situation in which there are huge masses of data but there is not (and, probably, there won't be for a long time) enough computing power to exhaustively process it. On one side, it is always more widely felt the need of extracting knowledge from databases, being such phase considered as a critical activity in many decision making processes. Such databases are actually considered as great potential knowledge repositories in a dormant state. Computational Statistics and, more properly, Data Mining techniques are, probably, the unique way to extract such knowledge from the forementioned raw data sources and this explains the always growing interest around such methodologies. This has led Data Mining to meet other research fields like machine learning, operation research and, more in general artificial intelligence. One of the actually biggest problem that many Data Mining techniques have to deal with is combinatorial optimization that, in the past, has led many techniques to be taken apart and, now, makes them applicable only within certain bounds. Since other research fields like Artificial Intelligence have being (and still are) dealing with such problems, their contribute to statistics (and viceversa) has been very significative. An indicator of this phenomenon could be the introduction of Neural Networks in Statistics. This thesis tries to go in this direction, in particularly about the use of Nature inspired optimization algorithms, which have been proven to be powerful instruments for attacking many combinatorial optimization problems, when exhaustive enumeration and evaluation of all possible candidate solution to a problem is not computationally affordable. In particular, Nature inspired algorithms have been designed for attacking the combinatorial optimization problems that affects Classification and Regression Trees algorithms, which are considered powerful instruments for knowledge extraction and decision making support. A Java software for quickly building Classification and Regression Trees (by using a very fast procedure) has also been written to fulfill the need for a flexible framework to support the research for developing the nature inspired algorithms. A Forward Search-based methodology has also been proposed to improve the stability of the Trees.

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