Evaluating Case-Base Maintenance algorithms

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted Manuscript version of a Published Work that appeared in final form in [Knowledge-Based Systems].he success of a Case-Based Reasoning (CBR) system closely depends on its knowledge-base, named the case-base. The life cycle of CBR systems usually implies updating the case-base with new cases. However, it also implies removing useless cases for reasons of efficiency. This process is known as Case-Base Maintenance (CBM) and, in recent decades, great efforts have been made to automatise this process using different kind of algorithms (deterministic and non-deterministic). Indeed, CBR system designers find it difficult to choose from the wealth of algorithms available to maintain the case-base. Despite the importance of such a key decision, little attention has been paid to evaluating these algorithms. Although classical validation methods have been used, such as Cross-Validation and Hold-Out, they are not always valid for non-deterministic algorithms. In this work, we analyse this problem from a methodological point of view, providing an exhaustive review of these evaluation methods supported by experimentation. We also propose a specific methodology for evaluating Case-Base Maintenance algorithms (the αβ evaluation). Experiment results show that this method is the most suitable for evaluating most of the algorithms and datasets studied

Monitoring elderly people at home with temporal Case-Based Reasoning

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted Manuscript version of a Published Work that appeared in final form in [Knowledge-Based Systems].This paper presents a study of why and how Case-Based Reasoning (CBR) can be used in the long term to help elderly people living alone in a Smart Home. The work focuses on the need to manage the temporal dimension and how the system must be maintained. The proposal involves the integration of a CBR system in a commercial Smart Home architecture that includes sensors, data communication and data integration. The CBR system analyses the daily activity at home as temporal event sequences, using temporal edit distance to identify the most similar cases. Most common Case-Based Maintenance (CBM) algorithms adapted to the temporal problem (t-CNN, t-RENN, t-ICF, t-DROP1 and t-RCFP) are used to reduce the number of cases in the case base in order to contribute to its long term maintenance. The experiments carried out analyse the effect of different temporal CBM algorithms in common risk scenarios (waking up during the night, falls and falls with loss of consciousness). Data experiments are generated synthetically based on real behaviour patterns of 12 hours’ and 24 hours’ duration. Algorithms are compared using a paired t-test analysis. The results show that the algorithms t-CNN and t-DROP1 are able to create case-bases that statistically present the same average results as the original case-base but with a 10–20% in size. Algorithms t-ICF, t-RCFP and t-RENN can build similar case-bases to the original with a 10–50% size reduction, although they are not totally equivalent since they have significantly different average results than the original case-base. Finally, algorithm t-RENN does not significantly reduce the size of the case-base because it commonly deletes cases describing abnormal scenarios. We demonstrate that the proposed temporal CBR system is able to detect the different proposed risk scenarios when there is a large number of cases. That is, the CBR systems are useful in the long term. Experiments indicate that the temporal CBM algorithms analysed are able to reduce case-bases successfully to detect abnormal scenarios. However, success in creating a maintained case-base equivalent to the original depends on the number of cases