A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem

- The ABC classification problem is solved using a discrete artificial neural network.
- A randomized greedy multi-start algorithm is designed to train the neural network.
- The neurons' weights are finding by solving linear programming formulations.
- The designed classifier shows good generalization accuracy for benchmark datasets.
- The proposed algorithm can be straight applied to other multi-class classification problems.

In this paper we deal with the problem of designing a classifier able to learn the classification of existing units in inventory and then use it to classify new units according to their attributes in a multi-criteria ABC inventory classification environment. To solve this problem we design a multi-start constructive algorithm to train a discrete artificial neural network using a randomized greedy strategy to add neurons to the network hidden layer. The process of weights' searching for the neurons to be added is based on solving linear programming formulations. The computational experiments show that the proposed algorithm is much more efficient when the dual formulations are used to find the weights of the network neurons and that the obtained classifier has good levels of generalization accuracy. In addition, the proposed algorithm can be straight applied to other multi-class classification problems with more than three classes.