Automated evolutionary approach for the design of composite machine learning pipelines

the use of appropriate madm model for ranking the vendors of mci equipments using fuzzy approach

abstract nowadays, the science of decision making has been paid to more attention due to the complexity of the problems of suppliers selection. as known, one of the efficient tools in economic and human resources development is the extension of communication networks in developing countries. so, the proper selection of suppliers of tc equipments is of concern very much. in this study, a ...

investigating the feasibility of a proposed model for geometric design of deployable arch structures

deployable scissor type structures are composed of the so-called scissor-like elements (sles), which are connected to each other at an intermediate point through a pivotal connection and allow them to be folded into a compact bundle for storage or transport. several sles are connected to each other in order to form units with regular polygonal plan views. the sides and radii of the polygons are...

Evolutionary approach for automated component-based decision tree algorithm design

This paper proposes a framework for automated design of component-based decision tree algorithms. These algorithms are being constructed by interchanging components extracted from decision tree algorithms and their partial improvements. Manual selection of the best-suited algorithm for a specific problem is a complex task because of the huge algorithmic space derived from component-based design...

A Machine Learning Approach for Automated Geomorphic Map Generation

Intelligent, automated analysis of data is a critical task in modern data-intensive sciences. The work presented in this thesis is a contribution to this body of research, focusing on automatic geomorphic characterization of planetary surfaces (particularly Mars). We present a framework for automated generation of geomorphic maps from topographic data. Our approach first segments the topographi...

Machine Learning for Automated Reasoning