Feature Selection for Construction Organizational Competencies Impacting Performance

• Author(s) / Creator(s)

  • Tiruneh, Getaneh Gezahegne
  • Fayek, Aminah Robinson

• Organizational competencies have a significant influence on performance; therefore, it is vital that organizations in the construction industry assess and enhance their competencies in order to improve performance. The set of variables that captures construction organizational competencies is highly dimensional. Feature selection (FS) helps to reduce the dimensionality of data by using only a subset of variables to develop a model. The main objective of the research presented in this paper is the development of a fuzzy inference system (FIS) applying fuzzy c-means clustering (FCM) and FS using genetic algorithms (GAs). First, the parameters of FCM are optimized and used to develop an FIS. Then, the FIS is optimized using a GA. The root mean square error (RMSE) is used as a fitness function for GA optimization. This paper contributes an FS approach for construction engineering and management problems that are characterized by high dimensionality of feature space and few data instances.

• Date created

  2019-01-01

• Subjects / Keywords

  • Feature selection
  • Fuzzy c-means clustering
  • Genetic algorithm
  • Competency
  • Performance

• Type of Item

  Conference/Workshop Presentation

• DOI

  https://doi.org/10.7939/r3-n4de-tp98

• License

  © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.