A Rank Aggregation Algorithm for Ensemble of Multiple Feature Selection Techniques in Credit Risk Evaluation

Abstract

 In credit risk evaluation the accuracy of a classifier is very significant for classifying the high-risk loan applicants correctly. Feature selection is one way of improving the accuracy of a classifier. It provides the classifier with important and relevant features for model development. This study uses the ensemble of multiple feature ranking techniques for feature selection of credit data. It uses five individual rank based feature selection methods. It proposes a novel rank aggregation algorithm for combining the ranks of the individual feature selection methods of the ensemble. This algorithm uses the rank order along with the rank score of the features in the ranked list of each feature selection method for rank aggregation. The ensemble of multiple feature selection techniques uses the novel rank aggregation algorithm and selects the relevant features using the 80%, 60%, 40% and 20% thresholds from the top of the aggregated ranked list for building the C4.5, MLP, C4.5 based Bagging and MLP based Bagging models. It was observed that the performance of models using the ensemble of multiple feature selection techniques is better than the performance of 5 individual rank based feature selection methods. The average performance of all the models was observed as best for the ensemble of feature selection techniques at 60% threshold. Also, the bagging based models outperformed the individual models most significantly for the 60% threshold. This increase in performance is more significant from the fact that the number of features were reduced by 40% for building the highest performing models. This reduces the data dimensions and hence the overall data size phenomenally for model building. The use of the ensemble of feature selection techniques using the novel aggregation algorithm provided more accurate models which are simpler, faster and easy to interpret.

Authors and Affiliations

Shashi Dahiya, S. S Handa, N. P Singh

Keywords

Related Articles

The Fault Location Method Research of Three-Layer Network System

The fault location technology research of three-layer network system structure dynamic has important theoretic value and apparent engineering application value on exploring the fault detection and localization of the com...

 Mobile Learning-system usage: Scale development and empirical tests

 Mobile technologies have changed the shape of learning for learners, society, and education providers. Consequently, mobile learning has become a core component in modern education. Nevertheless, introducing mobile...

 Parallelization of 2-D IADE-DY Scheme on Geranium Cadcam Cluster for Heat Equation

 A parallel implementation of the Iterative Alternating Direction Explicit method by D’Yakonov (IADE-DY) for solving 2-D heat equation on a distributed system of Geranium Cadcam cluster (GCC) using the Message Passi...

 For a Better Coordination Between Students Learning Styles and Instructors Teaching Styles

 While learning has been in the main focus of a number of educators and researches, instructors’ teaching styles have received considerably less attention. When it comes to dependencies between learning styles and t...

  An Efficient Routing Protocol under Noisy Environment for Mobile Ad Hoc Networks using Fuzzy Logic

 A MANET is a collection of mobile nodes communicating and cooperating with each other to route a packet from the source to their destinations. A MANET is used to support dynamic routing strategies in absence of wir...

Download PDF file
  • EP ID EP123358
  • DOI 10.14569/IJARAI.2016.050901
  • Views 128
  • Downloads 0

How To Cite

Shashi Dahiya, S. S Handa, N. P Singh (2016).  A Rank Aggregation Algorithm for Ensemble of Multiple Feature Selection Techniques in Credit Risk Evaluation. International Journal of Advanced Research in Artificial Intelligence(IJARAI), 5(9), 1-8. https://europub.co.uk/articles/-A-123358