Difference between revisions of "Types of Decision Models - Group 4 - 2018/2019, Semester B, Quartile 3"
| Line 24: | Line 24: | ||
= Decision Model Investigation = | = 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. | 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. | ||
Revision as of 15:40, 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">
- Page navigation
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
Back to the root page.
References
- ↑ "Brilliant.org: K-nearest Neighbors", Retrieved 17 March 2019