Studia Informatica Pomerania

Previously: Zeszyty Naukowe Uniwersytetu Szczecińskiego. Studia Informatica


ISSN: 2451-0424     eISSN: 2300-410X    OAI    DOI: 10.18276/si.2016.39-05
CC BY-SA   Open Access 

Issue archive / nr 39
Algorytmy do konstruowania drzew decyzyjnych w przewidywaniu skuteczności kampanii telemarketingowej banku
(Algorithms for constructing decision trees for predicting the effectiveness of the bank’s telemarketing campaign)

Authors: Jan Kozak
Uniwersytet Ekonomiczny w Katowicach, Wydział Informatyki i Komunikacji

Przemysław Juszczuk
Uniwersytet Śląski w Katowicach, Wydział Informatyki i Nauki o Materiałach
Keywords: decision trees data analysis telemarketing campaign
Whole issue publication date:2016
Page range:11 (49-59)
Cited-by (Crossref) ?:

Abstract

In this article we propose a novel approach for the generating transaction systems based on the technical analysis indicator - moving averages. Crossover of the moving average with the price chart is considered as a signal. Mechanism of setting the moving average period will be decreased in case of efficient trading. On the other hand, a couple of loss making trades leads to the increasing the moving average period. This will directly affect of decreasing number of trades. Such approach will be compared with the classical solutions based on crossover of two moving averages. Such mechanism will be presented as a system based on the procedural programming paradigm, in which stand-alone block codes are system functions. This will allow to easily expand some system functionalities without increasing code complexity.
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Bibliography

1.Boryczka, U., Kozak, J. (2014). On-the-go adaptability in the new ant colony decision forest approach. In: Intelligent Information and Database Systems. Intelligent Information and Database Systems – 6th Asian Conference, ACIIDS 2014, Bangkok, Thailand, April 7–9, 2014, Proceedings, Part II. Springer International Publishing, 157–166.
2.Bouckaert, R.R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., Scuse, D. (2013). Weka manual for version 3-7-10.
3.Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984). Classification and Regression Trees. New York: Chapman & Hall.
4.Elsalamony, H.A. (2014). Bank direct marketing analysis of data mining techniques. Network 5,0. International Journal of Computer Applications (0975–8887), 85 (7), 12–22.
5.Kass, G.V. (1980). An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society. Series C (Applied Statistics), 29 (2), 119–127.
6.Koronacki, J., Ćwik, J. (2008). Statystyczne systemy uczące się. Warszawa: Exit.
7.Kozak, J. (2011). Algorytmy mrowiskowe do konstruowania drzew decyzyjnych. Nieopublikowana praca doktorska.
8.Kozak, J., Boryczka, U. (2013). Dynamic version of the acdt/acdf algorithm for h-bond data set analysis. Computational Collective Intelligence. Technologies and Applications – 5th International Conference. Craiova, Romania, September 11–13, Proceedings, 701–710.
9.Lim, T.-S., Loh, W.-Y., Shih, Y.-S. (2000). A comparison of prediction accuracy, complexity, and training time of thirtythree old and new classification algorithms. Machine Learning, 40 (3), 203–228.
10.Loh, W.Y., Shih, Y.S. (1997). Split selection methods for classification trees. Statistica Sinica, 815–840.
11.Moro, S., Cortez, P., Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22–31.
12.Moro, S., Laureano, R., Cortez, P. (2011). Using data mining for bank direct marketing: An application of the crisp-dm methodology. Conference-ESM’2011. Eurosis, 117–121.
13.Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1 (1), 81–106.
14.Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
15.Quinlan, J.R. (1996). Improved use of continuous attributes in c4.5. Journal of Artificial Intelligence Research, 4, 77–90.
16.Rokach, L., Maimon, O. (2008). Data Mining With Decision Trees: Theory And Applications. River Edge, NJ, USA: World Scientific Publishing.
17.Timofeev, R. (2004). Classification and Regression Trees (CART) Theory and Applications. Master’s thesis, Berlin: CASE Humboldt University.
18.Łapczyński, M. (2003). Drzewa klasyfikacyjne w badaniach satysfakcji i lojalności klientów. W: Analiza satysfakcji i lojalności klientów. Zastosowania statystyki i data mining, 93 –102. Kraków: AE w Krakowie.