Discovering the classification rules for Egyptian stock market using genetic programming

Applications of learning algorithms in knowledge discovery are promising and relevant area of research. It is offering new possibilities and benefits in real-world applications, helping us understand better mechanisms of our own methods of knowledge acquisition. Genetic programming (GP) possess certain advantages that make it suitable for discovering the classification rule for data mining applications, such as convenient structure for rule generation. This paper, intended to discover classification rules for the Egyptian stock market return by applying GP. Since the Egyptian stock market return data have a large number of specific properties that together makes the generalized classification rules unusual. The process behaves very much like a random-walk process and regime shift in the sense that the underlying process is time varying. These reasons cause greats problems for the traditional classification algorithms. Experiments presenting a preliminary result to demonstrate the capability of GP to mine accurate classification rules suitable for prediction, comparable to traditional machine learning algorithms i.e., C4.5.