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

From Control Systems Technology Group

(Difference between revisions)
Jump to: navigation, search
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  
+
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]]
----
----

Revision as of 13:48, 17 March 2019

<link rel="shortcut icon" href="https://www.tue.nl/favicon-64.ico" type="image/x-icon"> <link rel=http://cstwiki.wtb.tue.nl/index.php?title=PRE2018_3_Group4&action=edit"stylesheet" type="text/css" href="theme.css">

Contents

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


Back to the root page.

References

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