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

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.

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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.

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Parametric action decision trees: Incorporating continuous attribute variables into rule-based models of discrete choice

Transportation Research Part B Methodological ◽

10.1016/j.trb.2007.01.001 ◽

2007 ◽

Vol 41 (7) ◽

pp. 772-783 ◽

Cited By ~ 31

Author(s):

Theo Arentze ◽

Harry Timmermans

Keyword(s):

Decision Trees ◽

Discrete Choice ◽

Rule Based ◽

Parametric Action ◽

Continuous Attribute ◽

Action Decision

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The Impact of Simplification in a Sequential Rule-Based Model of Activity-Scheduling Behavior

Environment and Planning A Economy and Space ◽

10.1068/a36167 ◽

2005 ◽

Vol 37 (3) ◽

pp. 551-568 ◽

Cited By ~ 7

Author(s):

Elke A L M G Moons ◽

Geert P M Wets ◽

Marc Aerts ◽

Theo A Arentze ◽

Harry J P Timmermans

Keyword(s):

Decision Trees ◽

Travel Behavior ◽

Activity Pattern ◽

Correlation Coefficients ◽

Predictive Performance ◽

Activity Scheduling ◽

Sequential Model ◽

Model Based ◽

Sequential Rule ◽

The Impact

The aim of this paper is to gain a better understanding of the impact of simplification on a sequential model of activity-scheduling behavior which uses feature-selection methods. To that effect, the predictive performance of the Albatross model, which incorporates nine different facets of activity–travel behavior, based on the original full decision trees, is compared with the performance of the model based on trimmed decision trees. The results indicate that significantly smaller decision trees can be used for modeling the different choice facets of the sequential model system without losing much in predictive power. The performance of the models is compared at three levels: the choice-facet level, the activity-pattern level (comparing the observed and generated sequences of activities), and the trip-matrix level, comparing the correlation coefficients that determine the strength of the associations between the observed and the predicted origin–destination matrices. The results indicate that the model based on the trimmed decision trees predicts activity-diary schedules with a minimum loss of accuracy at the decision level. Moreover, the results indicate a slightly better performance at the activity-pattern and the trip-matrix level.

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