A Comparative Study on the Use of Classification Algorithms in Financial Forecasting
作者： Fernando E. B. Otero , Michael Kampouridis
DOI: 10.1007/978-3-662-45523-4_23
关键词: Statistical classification 、 Class (biology) 、 Ant colony optimization algorithms 、 Computational finance 、 Computer science 、 Data mining 、 Genetic programming 、 Artificial intelligence 、 Financial forecasting 、 Genetic programming algorithm 、 Machine learning
摘要: Financial forecasting is a vital area in computational finance, where several studies have taken place over the years. One way of viewing financial as classification problem, goal to find model that represents predictive relationships between predictor attribute values and class values. In this paper we present comparative study two bio-inspired algorithms, genetic programming algorithm especially designed for forecasting, an ant colony optimization one, which problems. addition, compare above algorithms with other state-of-the-art namely C4.5 RIPPER. Results show very successful, significantly outperforming all given problems, provides insights improving design specific algorithms.
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