CBR-KM: Integració de mecanismes de manteniment de la memòria de casos per millorar el rendiment en el CBR

Abstract

The maintanence of a case data base is very important since it helps to improve efficiency in the Case-Based Reasoning (CBR). There are different types of maintenance and many algorithms, and the combination of both can make the CBR a very powerful tool. This Final Project consists on the implementation of different algorithms of memory of cases maintaining those that already exist in literature and that are applied on Case Based Reasoning systems. Specifically this project adds the following algorithms to the CBR_KM library: Repeated Edited Nearest Neighbor (RENN), All k-NN (ANN), Blame-Based Noise Reduction (BBNR) and Conservative Redundary Reduction (CRR). All these are pre-process algorithms, which are applied before the use of the base case in order to reduce its size removing cases that are considered harmful or less useful. The policy of each algorithm is different and it is focused on a particular aspect. The evaluation of algorithms will be made with different bases of cases from the UCI Repository and the results will be evaluated taking into account several criteria such as: final size of the case base, accuracy, rate of cases retention and percentage of forgotten cases.