A novel artificial immune system-based approach for mining associative classification rules with stock trading data

2017 ◽  
Vol 8 (3) ◽  
pp. 149 ◽  
Author(s):  
Mahsa Mahboob Ghodsi ◽  
M. Zandieh
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Classification Rules ◽  
Associative Classification ◽  
Artificial Immune ◽  
Stock Trading

Associative Classification With Artificial Immune System

2009 ◽  
Vol 13 (2) ◽  
pp. 217-228 ◽  
Author(s):  
Tien Dung Do ◽  
Siu Cheung Hui ◽  
A.C.M. Fong ◽  
B. Fong
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Associative Classification ◽  
Artificial Immune

scholarly journals AISAC: An Artificial Immune System for Associative Classification Applied to Breast Cancer Detection

2020 ◽  
Vol 10 (2) ◽  
pp. 515
Author(s):  
David González-Patiño ◽  
Yenny Villuendas-Rey ◽  
Amadeo José Argüelles-Cruz ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Breast Cancer ◽  
Immune System ◽  
Cancer Detection ◽  
Artificial Immune System ◽  
Breast Cancer Detection ◽  
Wilcoxon Test ◽  
Correct Identification ◽  
Associative Classification ◽  
Artificial Immune ◽  
Proposed Model

Early breast cancer diagnosis is crucial, as it can prevent further complications and save the life of the patient by treating the disease at its most curable stage. In this paper, we propose a new artificial immune system model for associative classification with competitive performance for breast cancer detection. The proposed model has its foundations in the biological immune system; it mimics the detection skills of the immune system to provide correct identification of antigens. The Wilcoxon test was used to identify the statistically significant differences between our proposal and other classification algorithms based on the same bio-inspired model. These statistical tests evidenced the enhanced performance shown by the proposed model by outperforming other immune-based algorithms. The proposed model proved to be competitive with respect to other well-known classification models. In addition, the model benefits from a low computational cost. The success of this model for classification tasks shows that swarm intelligence is useful for this kind of problem, and that it is not limited to optimization tasks.

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