An immune-inspired semi-supervised algorithm for breast cancer diagnosis

• In this paper, we seamlessly integrate the state-of-the-art in life science and artificial intelligence, and investigate a semi-supervised learning algorithm to reduce the need for labeled data. In the proposed algorithm, the Kent chaotic helps to search the best solution in the whole antibody cells feature vector space. Considering that the value of k is sensitive to the experiment results, we use the weighted k nearest neighbor algorithm to diagnose the breast cancer.• We used two well-known benchmark breast cancer datasets in our study, which were acquired from the UCI machine learning repository.
Source: Computer Methods and Programs in Biomedicine - Category: Bioinformatics Authors: Source Type: research