In this paper we present a new perspective on noise reduction for nearest-neighbour classifiers. Classic noise reduction algorithms such as Repeated Edited Nearest Neighbour remove cases from the training set if they are ...

The problem of concept drift has recently received considerable attention in machine learning research. One important practical problem where concept drift needs to be addressed is spam filtering. The literature on ...

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown ...

Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of ...

One of the perceived benefits of Case-Based Reasoning (CBR) is the potential to use retrieved cases to explain predictions. Surprisingly, this aspect of CBR has not been much researched. There has been some early work on ...

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of ?the curse ...

The ranking of offers is an issue in e-commerce that has received a lot of attention in Case-Based Reasoning research. In the absence of a sales assistant, it is important to provide a facility that will bring suitable ...

In Wrapper based feature selection, the more states that are visited during the search phase of the algorithm the greater the likelihood of finding a feature subset that has a high internal accuracy while generalizing ...