On using Bayesian networks for complexity reduction in decision trees

In the “Once-ler Problem,” the decision tree is introduced as a very useful technique that can be used to answer a variety of questions and assist in making decisions. This chapter builds on the “Lorax Problem” introduced in Chapter 19, where Bayesian networks were introduced. A decision tree is a graphical representation of the alternatives in a decision. It is closely related to Bayesian networks except that the decision problem takes the shape of a tree instead. The tree itself consists of decision nodes, chance nodes, and end nodes, which provide an outcome. In a decision tree, probabilities associated with chance nodes are conditional probabilities, which Bayes’ Theorem can be used to estimate or update. The calculation of expected values (or expected utility) of competing alternative decisions is provided on a step-by-step basis with an example from The Lorax.

Download Full-text

Bayesian Networks

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch018 ◽

2011 ◽

pp. 89-93 ◽

Cited By ~ 1

Author(s):

Ahmad Bashir ◽

Latifur Khan ◽

Mamoun Awad

Keyword(s):

Neural Networks ◽

Bayesian Networks ◽

Bayesian Network ◽

Decision Trees ◽

Graphical Model ◽

Conditional Probabilities ◽

Rule Bases

A Bayesian network is a graphical model that finds probabilistic relationships among variables of a system. The basic components of a Bayesian network include a set of nodes, each representing a unique variable in the system, their inter-relations, as indicated graphically by edges, and associated probability values. By using these probabilities, termed conditional probabilities, and their interrelations, we can reason and calculate unknown probabilities. Furthermore, Bayesian networks have distinct advantages compared to other methods, such as neural networks, decision trees, and rule bases, which we shall discuss in this paper.

Download Full-text

Bayesian networks and decision trees in the diagnosis of female urinary incontinence

Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143) ◽

10.1109/iembs.2000.900799 ◽

2002 ◽

Cited By ~ 2

Author(s):

M. Hunt ◽

B. von Konsky ◽

S. Venkatesh ◽

P. Petros

Keyword(s):

Urinary Incontinence ◽

Bayesian Networks ◽

Decision Trees ◽

Female Urinary Incontinence

Download Full-text

Integrating Bayesian networks and decision trees in a sequential rule-based transportation model

European Journal of Operational Research ◽

10.1016/j.ejor.2005.03.022 ◽

2006 ◽

Vol 175 (1) ◽

pp. 16-34 ◽

Cited By ~ 63

Author(s):

Davy Janssens ◽

Geert Wets ◽

Tom Brijs ◽

Koen Vanhoof ◽

Theo Arentze ◽

...

Keyword(s):

Bayesian Networks ◽

Decision Trees ◽

Transportation Model ◽

Rule Based ◽

Sequential Rule

Download Full-text

Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees

International Journal of Electrical Power & Energy Systems ◽

10.1016/j.ijepes.2011.09.009 ◽

2012 ◽

Vol 34 (1) ◽

pp. 90-98 ◽

Cited By ~ 63

Author(s):

Iñigo Monedero ◽

Félix Biscarri ◽

Carlos León ◽

Juan I. Guerrero ◽

Jesús Biscarri ◽

...

Keyword(s):

Bayesian Networks ◽

Decision Trees ◽

Power Utility ◽

Pearson Coefficient

Download Full-text

Combining Bayesian Networks and Decision Trees to Predict Drosophila melanogaster Protein-Protein Interactions

21st International Conference on Data Engineering Workshops (ICDEW'05) ◽

10.1109/icde.2005.205 ◽

2005 ◽

Author(s):

Jingkai Yu ◽

F. Fotouhi ◽

R.L. Finley

Keyword(s):

Drosophila Melanogaster ◽

Bayesian Networks ◽

Decision Trees ◽

Protein Interactions ◽

Protein Protein Interactions

Download Full-text

Synthesis of Supervised Approaches for Intrusion Detection Systems

Network Security Technologies - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-4666-4789-3.ch003 ◽

2014 ◽

pp. 44-57

Author(s):

Ahmed Chaouki Lokbani ◽

Ahmed Lehireche ◽

Reda Mohamed Hamou ◽

Abdelmalek Amine

Keyword(s):

Data Mining ◽

Network Security ◽

Intrusion Detection ◽

Bayesian Networks ◽

Decision Trees ◽

Association Rules ◽

Intrusion Detection Systems ◽

Data Mining Techniques ◽

Evaluation Measures ◽

Detection Systems

Given the increasing number of users of computer systems and networks, it is difficult to know the profile of the latter, and therefore, intrusion has become a highly prized area of network security. In this chapter, to address the issues mentioned above, the authors use data mining techniques, namely association rules, decision trees, and Bayesian networks. The results obtained on the KDD'99 benchmark have been validated by several evaluation measures and are promising and provide access to other techniques and hybridization to improve the security and confidentiality in the field.

Download Full-text