Utilization of Multi Attribute Decision Making Techniques to Integrate Automatic and Manual Ranking of Options

An information fusion system with local sensors sometimes requires the capability to represent the temporal changes of uncertain sensory information in dynamic and uncertain situation to access to a hypothesis node which cannot be observed directly. One of the central issue and challenging problem is the decision of what combination and order of sensors allocation should be selected between sensors, in order to maximize the global gain in the flow of information, when the data association is limited. In this area, Bayesian Networks (BNs) can constitute a coherent fusion structure and introduce different options (the combination of sensors allocation) for achieving to the hypothesis node through a number of intermediate nodes that are interrelated by cause and effect. BNs can rank the options in terms of their probabilities from Bayes' theorem calculation. But, decision making based on probabilities and numerical representations might not be appropriate. Thus, re-ranking the set of options based on multiple criteria such as those of multi-criteria decision aid (MCDA) should be ideally considered. Re-ranking and selecting the appropriate options are considered as a multi-attribute decision making (MADM) problem by user interaction as semi-automatically decision support. In this paper, Multi Attribute Decision Making (MADM) techniques as TOPSIS, SAW, and Mixed (Rank Average) for decision-making as well as AHP and Entropy for obtaining the weights of attributes have been used. Since MADM techniques give most probably different results according to different approaches and assumptions in the same problem, statistical analysis done on them. According to the results, the correlation between compared techniques for re-ranking BN options is strong and positive because of the close proximity of weights suggested by AHP and Entropy. Mixed method as compared to TOPSIS and SAW is the preferred technique when there is no historical (real) decision- making case; moreover, AHP is more acceptable than Entropy for weighting.

[1]  Mehrdad Mohammadi,et al.  Multi-Criteria Decision-Making Selection Model with Application to Chemical Engineering Management Decisions , 2009 .

[2]  Herry Purnomo,et al.  Guidelines for applying multi-criteria analysis to the assessment of criteria and indicators , 1999 .

[3]  Jana Iezzi,et al.  Multi-criteria decision making in outpatient scheduling , 2006 .

[4]  Jing Li,et al.  Optimal sensor allocation by integrating causal models and set-covering algorithms , 2010 .

[5]  M. Bohanec,et al.  The Analytic Hierarchy Process , 2004 .

[6]  Amin Karami,et al.  UTILIZATION AND COMPARISON OF MULTI ATTRIBUTE DECISION MAKING TECHNIQUES TO RANK BAYESIAN NETWORK OPTIONS Master Degree Project in Informatics One year Level ECTS 30 Spring term Year 2011 , 2011 .

[7]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[8]  Malur K. Sundareshan,et al.  Probabilistic Neural Network-Based Sensor Configuration in a Wireless Ad Hoc Network , 2004 .

[9]  Tom Ziemke,et al.  On the Definition of Information Fusion as a Field of Research , 2007 .

[10]  Kieren Jamieson,et al.  Information systems decision making factors affecting decision makers and outcomes , 2007 .

[11]  Olivier Stasse,et al.  A Visual Attention Framework for Search Behavior by a Humanoid Robot , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[12]  Wichian Premchaiswadi,et al.  Group Decision Making Using Bayesian Network Inference with Qualitative Expert Knowledge , 2010 .

[13]  S.A. Khaparde,et al.  Multi-attribute decision making approach for strategic planning of DGs , 2005, IEEE Power Engineering Society General Meeting, 2005.

[14]  C. Yeh.  A Problem‐based Selection of Multi‐attribute Decision‐making Methods , 2002 .

[15]  Aura Reggiani,et al.  AN APPLICATION OF THE MULTIPLE CRITERIA DECISION MAKING (MCDM) ANALYSIS TO THE SELECTION OF A NEW HUB AIRPORT , 2002 .

[16]  Napsiah Ismail,et al.  Use of Analytical Hierarchy Process (AHP) for Selecting The Best Design Concept , 2008 .

[17]  Ronnie Johansson,et al.  Information acquisition strategies for Bayesian network-based decision support , 2010, 2010 13th International Conference on Information Fusion.

[18]  Vincent W. S. Wong,et al.  Comparison between Vertical Handoff Decision Algorithms for Heterogeneous Wireless Networks , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[19]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[20]  Yun Peng,et al.  A Bayesian network based framework for multi-criteria decision making , 2004 .

[21]  Norman E. Fenton,et al.  Making decisions: using Bayesian nets and MCDA , 2001, Knowl. Based Syst..

[22]  Janusz Kacprzyk,et al.  Distances between intuitionistic fuzzy sets , 2000, Fuzzy Sets Syst..

[23]  Cornel Resteanu,et al.  A New Portfolio Selection Method Based on Interval Data , 2010 .

[24]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[25]  Tom Ziemke,et al.  Information fusion: a decision support perspective , 2007, 2007 10th International Conference on Information Fusion.

[26]  J.A. Lopez-Orozco,et al.  Unified fusion system based on Bayesian networks for autonomous mobile robots , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[27]  W. Marsden.  I and J , 2012 .

[28]  Jean Roy,et al.  Concepts, Models, and Tools for Information Fusion , 2007 .