Capturing the Base Station by Feature Engineering
Examensarbete för masterexamen
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Bibliographical item details
|Type: ||Examensarbete för masterexamen|
|Title: ||Capturing the Base Station by Feature Engineering|
|Authors: ||Helgegren, Rikard|
|Abstract: ||A feature vector is a compact representation of an object or system, that contains
the most important or informative aspects of the entity. A feature vector is often
used in the context of machine learning due to the effectiveness and noise reduction,
but can also be used for data exploration.
In this thesis we want to investigate if a complex system such as a base station can be
represented with a feature vector in an useful manner. To create the feature vector,
we ask subject matter experts for the most relevant attributes of the base station.
The feature vector is evaluated by partitioning all the base stations, either with an
unsupervised clustering algorithm, or by an interesting attribute of the base station.
The partitions are then visualized and presented to experts, who determined if the
partitions brings forth interesting patterns that can be useful, or if the partitions
are useful in themselves.
The result is a feature vector containing 494 features, based of 22 attributes that are
recommended by subject matter experts. The feature vector brings forth interesting
and useful patterns, and we can thus conclude that a feature vector can be used to
represent a complex system such as a base station in an useful manner.|
|Keywords: ||Feature vector, Visualisation, Clustering, Evaluation metrics, Base station.|
|Issue Date: ||2020|
|Publisher: ||Chalmers tekniska högskola / Institutionen för matematiska vetenskaper|
|Collection:||Examensarbeten för masterexamen // Master Theses|
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