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1

Application of instance-based learning for cast iron casting defects prediction

Sika Robert, Szajewski Damian, Hajkowski Jakub, Popielarski Paweł

Management and Production Engineering Review

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2019

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Vol. 10, No. 4

101--107

EN

The paper presents an example of Instance-Based Learning using a supervised classiﬁcation method of predicting selected ductile cast iron castings defects. The test used the algorithm of k-nearest neighbours, which was implemented in the authors’ computer application. To ensure its proper work it is necessary to have historical data of casting parameter values registered during casting processes in a foundry (mould sand, pouring process, chemical composition) as well as the percentage share of defective castings (unrepairable casting defects). The result of an algorithm is a report with ﬁve most possible scenarios in terms of occurrence of a cast iron casting defects and their quantity and occurrence percentage in the casts series. During the algorithm testing, weights were adjusted for independent variables involved in the dependent variables learning process. The algorithms used to process numerous data sets should be characterized by high eﬃciency, which should be a priority when designing applications to be implemented in industry. As it turns out in the presented mathematical instance-based learning, the best quality of ﬁt occurs for speciﬁc values of accepted weights (set #5) for number k = 5 nearest neighbours and taking into account the search criterion according to “product index”.

2

Mini-model method based on k-means clustering

Pietrzykowski M., Pluciński M.

Przegląd Elektrotechniczny

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2017

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R. 93, nr 1

73--76

EN

Mini-model method (MM-method) is an instance-based learning algorithm similarly as the k-nearest neighbor method, GRNN network or RBF network but its idea is different. MM operates only on data from the local neighborhood of a query. The paper presents new version of the MM-method which is based on k-means clustering algorithm. The domain of the model is calculated using k-means algorithm. Clustering method makes the learning procedure simpler.

PL

Metoda mini-modeli (metoda MM) jest algorytmem bazującym na próbkach podobnie jak metoda k-najbliższych sąsiadów, sieć RBF czy sieć GRNN ale jej zasada działania jest inna. MM operuje tylko na danych z najbliższego otoczenia punktu zapytania. Artykuł prezentuje nową wersję metody MM, która bazuje na algorytmie k-średnich. Domena MM jest obliczana przy pomocy algorytmu k-średnich. Użycie algorytmu klasteryzacji uprościło procedurę uczenia.

3

RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning

Góra G., Wojna A.

Fundamenta Informaticae

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2002

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Vol. 51, nr 4

369-390

EN

The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically induced during the learning phase. The empirical study shows the interesting fact that it is enough to consider a small neighbourhood to achieve classification accuracy comparable to an algorithm considering the whole learning set. The combination of k-NN and a rule-based algorithm results in a significant acceleration of the algorithm using all minimal rules. Moreover, the presented classifier has high accuracy for both kinds of domains: more suitable for k-NN classifiers and more suitable for rule based classifiers.