Abstract
The probability measure of regularities recognition in an information stream is introduced in the paper. The measure allows for the creation of machine-learning models without a supervisor. The experiment described in the paper empirically proves that the measure allows the recognition of regularities and helps to find out regular relations between the values of variables.
The machine learning model finds out regular relations in data set and by the model allow reconstructing unknown values of the classification variable. The classification algorithm on the basis of the probability measure of regularities recognition is described in the paper. The measure connection with entropy is demonstrated and mutual information is used to optimise the algorithm’s performance. The accuracy of the algorithm matches the accuracy of well-known supervised machine learning algorithms and also exceeds them.
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Mikheev, Y. (2019). The Measure of Regular Relations Recognition Applied to the Supervised Classification Task. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science(), vol 11943. Springer, Cham. https://doi.org/10.1007/978-3-030-37599-7_16
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