Abstract
A new approach to classifier synthesis was proposed by Polkowski and in this work we propose an implementation of this idea. The idea is based on usage of a dual indiscernibility matrix which allows to determine for each test object in the data, pairs of training objects which cover in a sense the given test object. A family of pairs best covering the given object pass their decisions for majority voting on decision for the test object. We present results obtained by our classifier on standard data from UCI Repository and compare them with results obtained by means of k-NN and Bayes classifiers. The results are validated by multiple cross-validation. We find our classifier on par with k-NN and Bayes classifiers.
In this work Sect. 1, Introduction, gives basic definitions of the notions applied and proposed method, Sect. 2 brings forth results of experiments with real data from UCI Repository. The last Sect. 3 is devoted to a discussion of results and concluding remarks.
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Acknowledgement
The research has been supported by grant 1309-802 from Ministry of Science and Higher Education of the Republic of Poland.
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Artiemjew, P., Nowak, B.A., Polkowski, L.T. (2016). A New Classifier Based on the Dual Indiscernibility Matrix. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2016. Communications in Computer and Information Science, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-46254-7_30
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DOI: https://doi.org/10.1007/978-3-319-46254-7_30
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