A Combinational Clustering Method Based on Artificial Immune System and Support Vector Machine

This paper focuses on a special category of machine learning problems arising in cases where the set of available training instances is significantly biased towards a particular class of patterns. Our work addresses the so-called Class Imbalance Problem through the utilization of an Artificial Immune System-(AIS)based classification algorithm which encodes the inherent ability of the Adaptive Immune System to mediate the exceptionally imbalanced “self” / “non-self” discrimination process. From a computational point of view, this process constitutes an extremely imbalanced pattern classification task since the vast majority of molecular patterns pertain to the “non-self” space. Our work focuses on investigating the effect of the class imbalance problem on the AIS-based classification algorithm by assessing its relative ability to deal with extremely skewed datasets when compared against two state-of-the-art machine learning paradigms such as Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs). To this end, we conducted a series of experiments on a music-related dataset where a small fraction of positive samples was to be recognized against the vast volume of negative samples. The results obtained indicate that the utilized bio-inspired classifier outperforms SVMs in detecting patterns from the minority class while its performance on the same task is competently close to the one exhibited by MLPs. Our findings suggest that the AIS-based classifier relies on its intrinsic resampling and class-balancing functionality in order to address the class imbalance problem.

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An Evolutionary Artificial Immune System for feature selection and parameters optimization of support vector machines for ERP assessment in a P300-based GKT

2008 Cairo International Biomedical Engineering Conference ◽

10.1109/cibec.2008.4786065 ◽

2008 ◽

Cited By ~ 4

Author(s):

S. Shojaie ◽

M. H. Moradi

Keyword(s):

Feature Selection ◽

Immune System ◽

Support Vector Machines ◽

Artificial Immune System ◽

Parameters Optimization ◽

Support Vector ◽

Artificial Immune ◽

Vector Machines

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An artificial immune system with bootstrap sampling for the diagnosis of recurrent endometrial cancers

Open Medicine ◽

10.1515/med-2021-0226 ◽

2021 ◽

Vol 16 (1) ◽

pp. 237-245

Author(s):

Chih-Yen Chang ◽

Yen-Chiao (Angel) Lu ◽

Wen-Chien Ting ◽

Tsu-Wang (David) Shen ◽

Wen-Chen Peng

Keyword(s):

Endometrial Cancer ◽

Immune System ◽

Unsupervised Learning ◽

Artificial Immune System ◽

Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Artificial Immune ◽

Bootstrap Sampling ◽

Recurrent Endometrial Cancer

Abstract Endometrial cancer is one of the most common gynecological malignancies in developed countries. The prevention of the recurrence of endometrial cancer has always been a clinical challenge. Endometrial cancer is asymptomatic in the early stage, and there remains a lack of time-series correlation patterns of clinical pathway transfer, recurrence, and treatment. In this study, the artificial immune system (AIS) combined with bootstrap sampling was compared with other machine learning techniques, which included both supervised and unsupervised learning categories. The back propagation neural network, support vector machine (SVM) with a radial basis function kernel, fuzzy c-means, and ant k-means were compared with the proposed method to verify the sensitivity and specificity of the datasets, and the important factors of recurrent endometrial cancer were predicted. In the unsupervised learning algorithms, the AIS algorithm had the highest accuracy (83.35%), sensitivity (77.35%), and specificity (92.31%); in supervised learning algorithms, the SVM algorithm had the highest accuracy (97.51%), sensitivity (95.02%), and specificity (99.29%). The results of our study showed that histology and chemotherapy are important factors affecting the prediction of recurrence. Finally, behavior code and radiotherapy for recurrent endometrial cancer are important factors for future adjuvant treatment.

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