The age at death assessment in a multi-ethnic sample of pelvic bones using nature-inspired data mining methods.

Recently published studies showed that age assessment methods are population specific. Authors analyse the senescence changes in pubic symphysis and sacro-pelvic surface of a pelvic bone using data mining methods. The multi-ethnic data set consists of 956 adult individuals ranging from 19 to 100 years of age derived from 9 different populations with known age and sex. The results show that accurate and reliable age assessment is possible to three age classes (less than 30, 30-60, 60 and more). The study confirms that population specificity of the methods exists and the variable "sex" is not important in age classification.

Meta-learning approach to neural network optimization.

Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply meta-learning principles to the neural network structure and function optimization. We show that diversity promotion, ensembling, self-organization and induction are beneficial for the problem. We combine several different neuron types trained by various optimization algorithms to build a supervised feed-forward neural network called Group of Adaptive Models Evolution (GAME). The approach was tested on a large number of benchmark data sets. The experiments show that the combination of different optimization algorithms in the network is the best choice when the performance is averaged over several real-world problems.