Feature extraction from discrete attributes
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Dosyalar
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we extract new features by combining k discrete attributes, where for each subset of size k of the attributes, we generate all orderings of values of those attributes exhaustively. We then apply the usual univariate decision tree classifier using these orderings as the new attributes. Our simulation results on 16 datasets from UCI repository [2] show that the novel decision tree classifier performs better than the proper in terms of error rate and tree complexity. The same idea can also be applied to other univariate rule learning algorithms such as C4.5Rules [7] and Ripper [3].
Açıklama
Anahtar Kelimeler
Data sets, Decision tree classifiers, Discrete attributes, Error rate, Rule learning algorithms, Simulation result, Tree complexity, UCI repository, Univariate, Classifiers, Decision trees, Learning algorithms, Feature extraction, Error analysis, Training, Impurities, Pattern recognition, Principal component analysis, Learning (artificial intelligence), Pattern classification, K discrete attributes, Univariate decision tree classifier, Univariate rule learning algorithms, C4.5 Rules, Ripper
Kaynak
Proceedings - International Conference on Pattern Recognition
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Yıldız, O. T. (2010). Feature extraction from discrete attributes. Paper presented at the Proceedings - International Conference on Pattern Recognition, 3915-3918. doi:10.1109/ICPR.2010.952
