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Explainability in RIONA Algorithm Combining Rule Induction and Instance-Based Learning

100%

Góra G. , Skowron A. , Wojna A.

Annals of Computer Science and Information Systems

2023

tom Vol. 35

491--502

The article concerns the well-known RIONA algorithm. We focus on the explainability property of this algorithm. The theoretical results, formulated and proved in the paper, show the relationships of the RIONA classifiers to both instance- and rule-based classifiers. In particular, we show the equivalence (relative to the classification) of the RIONA algorithm with the rule-based algorithm generating all consistent and maximally general rules from the neighbourhood of the test case. Consequently, the RIONA classifier can be represented by a rule-based classifier, with rules easily interpretable by humans. These theoretical results provide the explainability of the classifiers generated by RIONA and could be used in situations when an explanation or justification of the derived decision is important. It should be noted that the RIONA algorithm requires analysing only a small number of objects and rules contrary to algorithms based on the generation of huge sets of rules.

Methods of development fuzzy logic driven decision-support models in copper alloys processing

80%

Kluska-Nawarecka S. , Górny Z. , Mrzygłód B. , Wilk-Kołodziejczyk D. , Regulski K.

2010

tom Vol. 10, iss. 1 spec.

23--28

Development of a diagnostic decision support system using different then divalent logical formalism, in particular fuzzy logic, allows the inference from the facts presented not as explicit numbers, but described by linguistic variables such as the "high level", "low temperature", "too much content", etc. Thanks to this, process of inference resembles human manner in actual conditions of decision-making processes. Knowledge of experts allows him to discover the functions describing the relationship between the classification of a set of objects and their characteristics, on the basis of which it is possible to create a decision-making rules for classifying new objects of unknown classification so far. This process can be automated. Experimental studies conducted on copper alloys provide large amounts of data. Processing of these data can be greatly accelerated by the classification trees algorithms which provides classes that can be used in fuzzy inference model. Fuzzy logic also provides the flexibility of allocating to classes on the basis of membership functions (which is similar to events in real-world conditions). Decision-making in foundry operations often requires reliance on knowledge incomplete and ambiguous, hence that the conclusions from the data and facts may be "to some extent" true, and the technologist has to determine what level of confidence is acceptable, although the degree of accuracy for specific criteria is defined by membership function, which takes values from interval <0,1>. This paper describes the methodology and the process of developing fuzzy logic-based models of decision making based on preprocessed data with classification trees, where the needs of the diverse characteristics of copper alloys processing are the scope. Algorithms for automatic classification of the materials research work of copper alloys are clearly the nature of the innovative and promising hope for practical applications in this area.