Ammar, S.: Feature selection in possibilistic modeling. Pattern Recogn. 48, 3627-3640 (2015)S.A. Bouhamed, I.K. Kallel, D.S. Masmoudi, Feature selection in possibilistic modeling, Pattern Recognit. 48 (2015) 3627-3640.S. A. Bouhamed, I. K. Kallel, D. S. Masmoudi, and B. ...
Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation.doi:10.1117/12.2305178Joshua PeeplesDaniel SuenAlina ZareJames KellerSPIEInternational Conference on Multimedia Information Networking and Security
The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is presented as a technique to segment side-look synthetic aperture sonar (SAS) imagery into distinct regions of the sea-floor. In this work, we investigate and present the results of an automated feature selection approach...
Feature selectionCustomer churn has been considered asone of the key issues in the operations of the corporate business sector, as it influences the turnover directly. In particular, the telecom industries are seeking to develop new approaches to predict potential customer to churn. So, it needs ...
The selection of different points will also give different characteristics. Furthermore, points 𝑇1T1 and 𝑇2T2 are distinguishing points. The DFDV formed will be as many as the distinguishing points taken [26]. Two symbolic datasets were used in this research. The first is the symbolic ...
3.1. Parameter Selection The values of 𝜆λ and 𝛾γ are essential for the proposed algorithm since they affect the significance of the second and third terms in Equation (15) relative to the first term. Initially, 𝜆λ plays two roles in the clustering process; when 𝜆λ is large,...
3.1. Parameter Selection The values of 𝜆λ and 𝛾γ are essential for the proposed algorithm since they affect the significance of the second and third terms in Equation (15) relative to the first term. Initially, 𝜆λ plays two roles in the clustering process; when 𝜆λ is large,...