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DETERMINATION OF WEIGHTS FOR MULTI OBJECTIVE DECISION MAKING OR MACHINE LEARNING
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DETERMINATION OF WEIGHTS FOR MULTI-OBJECTIVE DECISION MAKING OR MACHINE LEARNING

Category : Software Engneering


Sub Category : DOTNET


Project Code : ITDSW06


Project Abstract

DETERMINATION OF WEIGHTS FOR MULTI-OBJECTIVE DECISION MAKING OR MACHINE LEARNING

                                 

ABSTRACT

                                                                                     

Decision-making as generally require the mechanisms to make the tradeoff among contradicting design criteria. When multiple objectives are involved in decision making or machine learning, a crucial step is to determine the weights of individual objectives to the system-level performance. Determining the weights of multi-objectives is an evaluation process, and it has been often treated as an optimization problem. However, our preliminary investigation has shown that existing methodologies in dealing with the weights of multi-objectives have some obvious limitations in the sense that the determination of weights is tackled as a single optimization problem, a result based on such an optimization is incomprehensive, and it can even be unreliable when the information about multiple objectives is incomplete such as an incompleteness caused by poor data. The constraints of weights are also discussed. Variable weights are natural in decision-making processes. Here, we need to develop a systematic methodology in determining variable weights of multi-objectives. The roles of weights in an original multi-objective decision-making or machine-learning problem are analyzed, and the weights are determined with the aid of a modular neural network.

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

In existing, the decision making problem to analyze the effect of variable weight synthesis. It is proved that the max-min decision-making model is the extreme of a class of variable weight synthesis models.

We research a class of multifactor decision making problems with variable weight theory, propose the idea of hierarchical variable weight, and establish a multifactor decision-making model based on this idea further. Finally, we discuss a real problem about selecting excellent person. The result shows that this model can satisfy the decision maker very much.

 

PROPOSED CONCEPT :-

Our modular neural networks of the decision-making process of complex systems have been discussed and the focus is directed on how to evaluate relative importance when multiple design criteria are involved.

The relative importance of one design criterion over another is presented by weights. It has been found that the determination of weights involves an evaluation process, which should not be simply treated as an optimization. A systematic methodology has been proposed to determine the weights for multiple objectives appropriately.

EXISTING TECHNIQUE:-

Multi-Objective Decision Making / Multi Functional Machine Learning

PROPOSED TECHNIQUE:-

Modular neural networks (MNN)

 

TECHNIQUE DEFINITION:-

The interpretation of "solving" could be choosing a small set of good alternatives, or grouping alternatives into different preference sets. The purpose is to support decision makers facing such problems.

TECHNIQUE DEFINITION:-

It is a series of independent neural networks moderated by some intermediary. He intermediary takes the outputs of each module and processes them to produce the output of the network as a whole.

DISADVANTAGES:-

Lack of poor weight determination and consistency.

Analytical model have only option to express weights.

ADVANTAGES:-

Consistency and weight determination as sensitivity.

System optimization and reliability.

 


 
 
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