Shahira Shaaban Azab Ahmed <br/><br/>
Semi-supervised classification using natural-based computation /  
 التصنيف شبه الإشرافي باستخدام الحوسبة المستوحاة من الطبيعة 
Shahira Shaaban Azab Ahmed ; Supervised Mohamed Farouk Abdelhady , Hesham Ahmed Hefny 
 - Cairo :   Shahira Shaaban Azab Ahmed ,   2017 
 - 146 Leaves :   charts , facsimiles ;   30cm 
<br/><br/>Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Science 
<br/><br/> This Thesis presents a cluster-and-label model using PSO to optimize the cluster centroid. In addition, labeled data are used to label cluster and guide clustering process. In some domains, the number of clusters in semi-supervised classification is unknown as in the Automatic Knowledgebase Construction. This thesis proposes an algorithm 2ESPSO3 to detect the number of clusters in the dataset by using PSO to optimize silhouette score. Then, the detected numbers of clusters are used in exploratory semi-supervised classification tasks with an unanticipated cluster 
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<br/><br/><label>Subjects--Index Terms: </label>Particle Swarm Optimization  Semi-supervised classification Swarm intelligence