Authors:
Artur Ferreira
1
;
2
and
Mário Figueiredo
3
;
2
Affiliations:
1
ISEL, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Portugal
;
2
Instituto de Telecomunicações, Lisboa, Portugal
;
3
IST, Instituto Superior Técnico, Universidade de Lisboa, Portugal
Keyword(s):
Bit Allocation, Classification, Explainability, Feature Discretization, Feature Selection, Machine Learning, Mutual Information, Supervised Learning.
Abstract:
In machine learning (ML) and data mining (DM) one often has to resort to data pre-processing techniques to achieve adequate data representations. Among these techniques, we find feature discretization (FD) and feature selection (FS), with many available methods for each one. The use of FD and FS techniques improves the data representation for ML and DM tasks. However, these techniques are usually applied in an independent way, that is, we may use a FD technique but not a FS technique or the opposite case. Using both FD and FS techniques in sequence, may not produce the most adequate results. In this paper, we propose a supervised discretization-selection technique; the discretization step is done in an incremental approach and keeps information regarding the features and the number of bits allocated per feature. Then, we apply a selection criterion based upon the discretization bins, yielding a discretized and dimensionality reduced dataset. We evaluate our technique on different typ
es of data and in most cases the discretized and reduced version of the data is the most suited version, achieving better classification performance, as compared to the use of the original features.
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