Empowering Simultaneous Feature and Instance Selection in Classification Problems through the Adaptation of Two Selection Algorithms

This paper proposes a new approach to data selection, a key issue in classification problems. This approach, which is based on a feature selection algorithm and one instance selection algorithm, reduces the original dataset in two dimensions, selecting relevant features and retaining important insta...

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Vydané v:2010 International Conference on Machine Learning and Applications s. 793 - 796
Hlavní autori: Ferreira do Carmo, Rafael Augusto, Gomes de Freitas, Fabricio, Teixeira de Souza, Jerffeson
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.12.2010
Predmet:
Algorithm design and analysis
classification
data
Data mining
Data models
Error analysis
feature
Focusing
instance
Machine learning
Machine learning algorithms
selection
ISBN:1424492114, 9781424492114
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Popis
Shrnutí:This paper proposes a new approach to data selection, a key issue in classification problems. This approach, which is based on a feature selection algorithm and one instance selection algorithm, reduces the original dataset in two dimensions, selecting relevant features and retaining important instances simultaneously. The search processes for the best feature and instance subsets occur separately yet, due to the influence of features in the importance of instances and vice versa, they bias one another. The experiments validate the proposed approach showing that this existing relation between features and instances can be reproduced when constructing data selection algorithms and that it leads to a quality improval comparing to the sequential execution of both algorithms.
ISBN:1424492114
9781424492114
DOI:10.1109/ICMLA.2010.121