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Title: Técnicas de Machine Learning y desarrollo de modelos predictivos aplicados a la antropología forense
Author: Álvarez Fernández, Noemi
Director: Merino Arranz, David
Tutor: Jordana Comin, Xavier
Others: Universitat Oberta de Catalunya
Keywords: artificial neural networks
machine learning
random forest
Issue Date: 5-Jun-2018
Publisher: Universitat Oberta de Catalunya
Abstract: This paper studies the application of Machine Learning techniques in the development of models for estimating the sex of human beings, within a forensic and bioarchaeological context. These techniques allow the development of algorithms that can learn and schematize properties and underlying structural patterns of the data, and can be used for specific information and predict specific phenomena. Therefore, given the importance of a reliable estimation of sex and the usefulness of Machine Learning techniques in the development of classification models for the prediction of categorical variables. What is offered in this work is the application of classical algorithms and Machine Learning for the construction of predictive models for sex estimation from metric studies of these remains. For this, in the first place, a bibliographic review of the Machine Learning and Statistics Classics techniques in the area of forensic anthropology was carried out. Obtaining a text where these methods are defined and compared. Once done, we evaluated and chose those who stayed better to our problem. Finally, three classification models were obtained with the methods of logistic regression, artificial neural networks and Random Forest, using the R software. The results were a series of powerful predictive models. Therefore, it can be said that Machine Learning techniques are a primising alternative to classical classification methods.
Language: Spanish
URI: http://hdl.handle.net/10609/82140
Appears in Collections:Bachelor thesis, research projects, etc.

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