Stable Feature Selection and Clustering According to Hierarchical Structures Based on Chaotic Multispecies Particle Swarm Optimization Applied for Genetic Data Diagnosis and Prognosis

Subject Areas : Information Technology in Engineering Design (ITED) Journal

Maryam yassi ¹ , Mohammad Hossein Moattarb ² , Mehdi Yaghoobi ³

1 - Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran
2 - Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad Iran
3 - Department of Artificial Intelligence, Mashhad Branch, Islamic Azad University, Mashhad Iran

Received: 2017-06-19 Accepted : 2017-06-19 Published : 2014-08-23

Keywords:

Abstract :

Any abnormal reproduction of cells is a tumor. censer happens when there’s an unstrained growth of abnormal cells. Cancer and tumors are divided in to two types, malignant and benign. Given the growth in the environmental information, it’s essential to employ some tools to analyze this data and gain the knowledge embedded in it. Since large-scale problems and huge data bases are incomprehensible for the human, employing intelligent methods is effective in understanding large-scale data better. In this paper, the integration methods are a subset of rating measures each with a specific objective of sustainable features for superior selection of distinct features.The next step would discuss creating a fuzzy system (FS) to detect and classify between benign and malignant nature of biological data. Fuzzy system type is Takagi-Sugeno-Kang (TSK). To classify a hierarchical structure of multi-species particle swarm algorithm based on chaotic particle can be used to optimize the fuzzy system. In addition, using chaotic theory discerns the true diversity of the particles and increases the power to detect and classify the samples. Accurate identification and classification of malignant and benign biological nature of the data is more than 95%. This simulation is performed on UCI and Microarray data-base.