Using artificial immune algorithm for fast convergence of multi layer perceptron in breast cancer diagnosis application

Rima Daoudi (1, 2) , Khalifa Djemal (2) , Abdelkader Benyettou (1)

1 SIMPA - Laboratoire Signal Image Parole
2 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes

In this paper, a Multi Layer Perceptron (MLP) based Artificial Immune System (AIS) is presented for breast cancer classification. The proposed algorithm integrates clonal selection principle of AIS in MLP learning to reduce its computational costs and accelerate its convergence to a Mean Squared Error Threshold (MSEth) set by the user. Applied on the Wisconsin Diagnosis Breast Cancer database (WDBC), the results show that combining Artificial Immune Systems and Neural Networks is effective. Indeed, a significant reduction of computation time has been obtained with a slight improvement of classification accuracy.

Mots clés: Multi Layer Perceptron; Artificial Immune System; Classification; Breast Cancer; Mean Squared Error; Speed Convergenc

Domaines: Traitement du signal et de l'image [eess.SP]

Rima Daoudi, Khalifa Djemal, Abdelkader Benyettou. Using artificial immune algorithm for fast convergence of multi layer perceptron in breast cancer diagnosis application. 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA 2015), Nov 2015, Orléans, France. pp.341-345, ⟨10.1109/IPTA.2015.7367161⟩. ⟨hal-01277528⟩