https://github.com/mpgon/master-dissertation

master dissertation dev
https://github.com/mpgon/master-dissertation

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master dissertation dev

• Host: GitHub
• URL: https://github.com/mpgon/master-dissertation
• Owner: mpgon
• Created: 2017-03-02T15:52:48.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-05-02T22:07:08.000Z (over 6 years ago)
• Last Synced: 2024-11-11T04:05:02.427Z (about 1 month ago)
• Language: TeX
• Homepage:
• Size: 60.1 MB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Label Ranking for Election Outcome Prediction (MSc)

[publication](http://hdl.handle.net/10216/111219)

Predicting the election outcome is a complex task. Currently used approaches, such as classic

pools, might not be able to capture the multitude of variables involved in the election results.

This work proposes tackling the problem of Election Outcome Prediction with the use

of Data Mining and Machine Learning. The focus will be in the study of the preferences

of the parties and their socio-economic context. To tackle this, we suggest to transform the

prediction of elections into a Label Ranking (LR) problem. 

LR is a subtask of the Preference Learning field, which uses a set of tools that allow the discovery of patterns in preferences.

This has the advantage of allowing the prediction of, not only who is going to win, but also

an ordered relation between the political parties or candidates.

In particular, we are going to focus on Pairwise Association Rules (PAR). We will use them for

prediction purposes. They come with the advantage that they provide interpretable results,

which is useful to analyze the predictions.

The results will be tested both in common LR datasets and in election datasets. We will

compare our approach with other LR algorithms.

In the end, considering the good results obtained, we believe that this work holds promise

both as a contribution to the LR community and the Political Science field.

## run

`R /src/core.R`