A new algorithm for generation of decision trees

• Autorzy: Grzymała-Busse J. W. , Hippe Z. S. , Knap M. , Mroczek T.
• Konferencja: Computers in Medical Applications: XIIIth Biocybernetics and Biomedical Engineering Conference (10-13 September 2003, Gdańsk, Poland)
• Języki publikacji: EN

Abstrakty

EN

A new algorithm for development of quasi-optimal decision trees, based on the Bayes theorem, has been created and tested. The algorithm generates a decision tree on the basis of Bayesian belief networks, created prior to the formation of the decision tree. The efficiency of this new algorithm was compared with three other known algorithms used to develop decision trees. The data set used for the experiments was a set of cases of skin lesions, histopatolgically verified.

Słowa kluczowe

EN

artificial intelligence; supervised machine learning; decision tree; Bayes networks

• Wydawca: Politechnika Gdańska
• Czasopismo: TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk
• Rocznik: 2004
• Tom: Vol. 8, No 2
• Strony: 243--247
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Grzymała-Busse J. W.

• Department of Electrical Engineering and Computer Science, Kansas University, Lawrence (KS) USA
• Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, Sucharskiego 2, 35-225 Rzeszow, Poland

autor

Hippe Z. S.

• Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, Sucharskiego 2, 35-225 Rzeszow, Poland

autor

Knap M.

• Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, Sucharskiego 2, 35-225 Rzeszow, Poland

autor

Mroczek T.

• Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, Sucharskiego 2, 35-225 Rzeszow, Poland

Bibliografia

• [1] Grzymała-Busse J W and Hippe Z S 2000 Advances in Soft Computing (Intelligent Information Systems) (Kłopotek M, Michalewicz M and Wierzchoń S T, Eds.), Physica-Verlag, Heidelberg, pp. 27–34
• [2] Hippe Z S and Błajdo P 2002 Methods of Artificial Intelligence (Burczyński T, Cholewa W and Moczulski W, Eds.), Silesian University of Technology Edit. Office, Gliwice, pp. 181–185
• [3] Lauria E J M and Tayi G K 2003 Data Mining: Opportunities and Challenges (Wang J, Ed.), Idea Group Publishing, Hershey (PA), pp. 260–277
• [4] Hippe Z S, Knap M and Paja W 2002 Methods of Artificial Intelligence (Burczyński T, Cholewa W and Moczulski W, Eds.), Silesian University of Technology Edit. Office, Gliwice, pp. 177–180
• [5] Hippe Z S and Wrzesień M 2002 Methods of Artificial Intelligence (Burczyński T, Cholewa W and Moczulski W, Eds.), Silesian University of Technology Edit. Office, Gliwice, pp. 185–189
• [6] Grzymała-Busse J W 1997 Fundameta Informaticae 31 27
• [7] Grzymała-Busse J W, Hippe Z S, Knap M and Mroczek T 2003 Knowledge Engineering and Expert Systems (Bubnicki Z and Grzech A, Eds.), Wroclaw University of Technology Edit. Office, Wroclaw 1, pp. 239–247 (in Polish)
• [8] Czerwiński Z 1970 Przegląd Statystyczny 1970/2, PWN, Warsaw (in Polish)
• [9] Quinlan J R 1993 C4.5 Programs for Machine Learning, Morgan Kaufmann, San Mateo (CA)
• [10] Hippe Z S and Mroczek T 2003 Computer Recognition Systems (Kurzyński M, Puchała E and Woźniak M, Eds.), Wroclaw University of Technology Edit. Office, Wroclaw, pp. 337–342
• [11] Hippe Z S and Knap M 2003 Algorithm for Generation of Decision Trees via Rules, Internal Report, Department of Expert Systems and Artificial Intelligence, WSIZ, Rzeszow (in Polish)
• [12] Jensen F V 2001 Bayesian Networks and Decision Graphs, Springer-Verlag, Heidelberg
• [13] Heckerman D 1999 A Tutorial on Learning with Bayesian Networks, Technical Report MSRTR-95-06
• [14] Hippe Z S 1999 TASK Quart. 4 483
• [15] Dąbrowski A 1974 On the Information Theory, WSiP, Warsaw (in Polish)