Large margin nearest neighbour regression using different optimization techniques
Florin Leon, Silvia Curteanu · Journal of Intelligent & Fuzzy Systems · 2016
The concept of a large margin is central to support vector machines and it has recently been adapted and applied for nearest neighbour classification. In this paper, a modification of this method is proposed in order to be used for regression problems. This model also allows the use of a set of prototypes with different distance metrics, which can increase the flexibility of the method especially for problems with large number of instances. The learning of the distance metrics is performed by two optimization methods, namely an evolutionary algorithm and an approximate differential approach. A real world problem, i.e. the prediction of the corrosion resistance of some alloys containing titanium and molybdenum is considered as a case study. It is shown that the suggested method provides very good results compared to other well-known regression algorithms.