Abstract
The maintenance of Case-Based Reasoning (CBR) systems has attracted increasing interest within current research since they proved high-quality results in different real-world domains. This kind of systems stores previous experiences, which are described by a vocabulary (e.g., attributes), incrementally in a case base. Actually, the vocabulary presents one among the most important maintenance targets, since it highly contributes in providing accurate solutions and in improving systems’ performance, especially within high-dimensional domains. However, there is no policy, in the literature, that offers the ability to exploit prior knowledge (e.g., given by domain-experts) during the maintenance of features describing cases. In this paper, we propose a flexible policy for the most relevant attribute selection based on the attribute clustering concept. This new policy is able, on the one hand, to manage uncertainty using the belief function theory based tools, and on the other hand, to make use of domain-experts knowledge in form of pairwise constraints: If two attributes offer the same information without any added-value, then a Must-link constraint between them is generated. Otherwise, if there is no relation between them and they offer different information, then a Cannot-link constraint between them is created.
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Notes
- 1.
Two attributes are surely belonging to the same cluster.
- 2.
Two attributes cannot belong to the same cluster.
- 3.
In our context, these objects represent the set of features that describe cases.
- 4.
In our policy, it concerns dissimilarity data between attributes, which are supplied from the previous step.
- 5.
Calling domain-experts to generate constraints presents one among our perspectives.
- 6.
During the experimentation, different values to set Thresh have been tested. The best results are offered with \(Thresh=0.55\).
- 7.
Sonar (SN), Ionosphere (IO), Glass (GL), BreastCancer (BC), German (GR), and Heart (HR): https://archive.ics.uci.edu/ml/.
- 8.
ReliefF-CBR: GL and GR (\(K=7\)); BC (\(K=8\)); SN, IO, and HR (\(K=9\));
EvAttClus: IO (\(K=3\)); HR (\(K=5\)); BC and GR (\(K=8\)); SN and GL (\(K=9\));
CEVM\(_{bat}\): IO (\(K=3\)); HR (\(K=4\)); BC and GR (\(K=8\)); GL and SN (\(K=9\));
CEVM\(_{alt}\): IO (\(K=3\)); HR (\(K=4\)); GR (\(K=6\)); GL (\(K=7\)); SN and BC (\(K=8\)).
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Ben Ayed, S., Elouedi, Z., Lefevre, E. (2019). CEVM: Constrained Evidential Vocabulary Maintenance Policy for CBR Systems. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_50
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