Abstract
Data sets in Bioinformatics usually present a high level of noise. Various processes involved in biological data collection and preparation may be responsible for the introduction of this noise, such as the imprecision inherent to laboratory experiments generating these data. Using noisy data in the induction of classifiers through Machine Learning techniques may harm the classifiers prediction performance. Therefore, the predictions of these classifiers may be used for guiding noise detection and removal. This work compares three approaches for the elimination of noisy data from Bioinformatics data sets using Machine Learning classifiers: the first is based in the removal of the detected noisy examples, the second tries to reclassify these data and the third technique, named hybrid, unifies the previous approaches.
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Miranda, A.L.B., Garcia, L.P.F., Carvalho, A.C.P.L.F., Lorena, A.C. (2009). Use of Classification Algorithms in Noise Detection and Elimination. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_50
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DOI: https://doi.org/10.1007/978-3-642-02319-4_50
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