# Types of Decision Models - Group 4 - 2018/2019, Semester B, Quartile 3

(Difference between revisions)
 Revision as of 13:48, 17 March 2019 (view source) (→Picking variables / attributes)← Older edit Revision as of 14:00, 17 March 2019 (view source) (→Picking variables / attributes)Newer edit → Line 32: Line 32: === Picking variables / attributes === === Picking variables / attributes === - In order for Nearest Neighbour to work, we need to quantify our problem into numerical values. For this, we need to split this up into variables with numerical data. This can be done in the same way as we picked the attributes in section [[Decision Model - Group 4 - 2018/2019, Semester B, Quartile 3#Attributes | implemented decision model #attributes]] + In order for Nearest Neighbour to work, we need to quantify our problem into numerical values. For this, we need to split this up into variables with numerical data. This can be done in the same way as we picked the attributes in section [[Decision Model - Group 4 - 2018/2019, Semester B, Quartile 3#Attributes | implemented decision model #attributes]]. These are variables that can tell which type of solution will fit best for this case. Examples of these attributes for the solution are e.g. cost (in €), reliability (in %), range (in m), hindrance to surroundings (scale from 1 to 10), CO2-emmision (in kg CO2 / year), etc. + + === How does NN work? === + So, we now have defined a solution in terms of only numerical variables. Then, for each solution that we have found, we will assign corresponding values to the attributes. An example of how this is done can be found + + How NN works as follows: it plots the points from the solutions in the n-dimensional plane, where n is the amount of variables / attributes that each solution consists of. We have that the first variable will correspond to the first coordinatec. Continuing this fashion, the second coordinate corresponds to the second variable or attribute, etc. Using these n variables or attributes that we will predetermine, we get a plot of the solutions the n dimensional plane. ---- ----

## Revision as of 14:00, 17 March 2019

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# Decision Model Investigation

In this section, we will investigate some different approaches for decision models. These decision models were investigated, but were chosen not to be the final decision model that we will implement. However, for the sake of completeness of this wiki, we will describe our findings on other decision models in this section.

## Nearest Neighbour Strategy

NearestNeighbour, short NN, is a mathematical decision model. It is a machine learning decision model, in the sense that existing solutions, often denoted as training data, are used for NN to be able to accurately make predictions about new data such as a user which wants a solution for their airport. This decision model can make the choice which solution fits best to the user. Nearest Neighbour is based on the machine learning strategy KNearestNeighbors [1].

### Picking variables / attributes

In order for Nearest Neighbour to work, we need to quantify our problem into numerical values. For this, we need to split this up into variables with numerical data. This can be done in the same way as we picked the attributes in section implemented decision model #attributes. These are variables that can tell which type of solution will fit best for this case. Examples of these attributes for the solution are e.g. cost (in €), reliability (in %), range (in m), hindrance to surroundings (scale from 1 to 10), CO2-emmision (in kg CO2 / year), etc.

### How does NN work?

So, we now have defined a solution in terms of only numerical variables. Then, for each solution that we have found, we will assign corresponding values to the attributes. An example of how this is done can be found

How NN works as follows: it plots the points from the solutions in the n-dimensional plane, where n is the amount of variables / attributes that each solution consists of. We have that the first variable will correspond to the first coordinatec. Continuing this fashion, the second coordinate corresponds to the second variable or attribute, etc. Using these n variables or attributes that we will predetermine, we get a plot of the solutions the n dimensional plane.

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# References

1. "Brilliant.org: K-nearest Neighbors", Retrieved 17 March 2019