Improving the H2MLVQ algorithm by the Cross Entropy Method


  • Abderrahmane Boubezoul
  • Sébastien Paris
  • Mustapha Ouladsine



Generalized Learning Vector Quantization, Relevance Learning, Cross Entropy method, Initialization sensitiveness, DDC: 004 (Data processing, computer science, computer systems)


This paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ) algorithm, this algorithm was proposed as an initialization insensitive variant of the well known Learning Vector Quantization (LVQ) algorithm. This paper has two aims, the first aim is the use of the Cross Entropy (CE) Method to tackle the initialization sensitiveness problem associated with the original (LVQ) algorithm and its variants and the second aim is to use a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, good competitive numerical results on several datasets are reported.