Affiliation:
1. Bozorgmehr University of Qaenat
2. Ferdowsi University of Mashhad
Abstract
Abstract
There are several cancer detection methods with their own disadvantages in flexibility, non-linear complexity and sensitive in imbalance data. In this paper, a new fuzzy bio-inspired based classification method is designed to classify the imbalance medical data. The method consists of a new fuzzy draft of Cuckoo Optimization Algorithm (COA) and separating hyper-planes based on assigning binary codes to separated regions that called Hyper-Planes Classifier (HPC). Based on the technical review is done in the paper, the HPC has a better structural superiority than the other classification algorithms. The Fuzzy Cuckoo Optimization Algorithm (FCOA) which fills up its challenge in proper tuning parameters, is proposed to optimize the weights of the separating hyper-planes with linear complexity time. The FCOA is designed based on a fuzzy inference system for the Egg Laying Radius (ELR) parameter setting to increase the efficiency of the generic COA. The proposed fuzzy bio-inspired based classification method is examined with four famous UCI cancer datasets based on one, two, three and four hyper-planes and compared with more than thirty previous researches. The results show that the proposed method is effective compared to the previous methods and also the COA.
Publisher
Research Square Platform LLC
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