• Corpus ID: 55983884

Fuzzified MCDM Consistent Ranking Feature Selection with Hybrid Algorithm for Credit Risk Assessment

@article{Tamilselvi2015FuzzifiedMC,
  title={Fuzzified MCDM Consistent Ranking Feature Selection with Hybrid Algorithm for Credit Risk Assessment},
  author={Y. Beulah Jeba Jaya and J. Jebamalar Tamilselvi},
  journal={Research Journal of Applied Sciences, Engineering and Technology},
  year={2015},
  volume={11}
}
Feature selection algorithms that are based on different single evaluation criterions for determining the subset of features shows varying result sets which lead to inconsistency in ranks. In contrary, Multiple Criteria Decision Making (MCDM) with Fuzzified Feature Selection methodology brings consistency in feature selection ranking with optimal features and improving the classification performance of credit risks. By adopting multiple evaluation criteria inconsistent ranks to Fuzzy Analytic… 

Figures and Tables from this paper

FUZZY MULTI-CRITERIA RANDOM SEED AND CUTOFF POINT APPROACH FOR CREDIT RISK ASSESSMENT

TLDR
This research work identifies the best cutoff point with respect to datasets and classifiers and integrates MCDM under fuzzy environment in all data mining stages of evaluation to take better decisions on multiple criteria, selection of initial random seed in the clustering phase for better cluster quality and Best Seed Clustering combined Classification with selected features to improve classification performance.

References

SHOWING 1-10 OF 31 REFERENCES

Simplified MCDM Analytical Weighted Model for Ranking Classifiers in Financial Risk Datasets

TLDR
The simplified MCDM model can be applied to rank the classifiers which use simplified backgrounds in making right decisions among multiple criteria, and Logistic regression and Bayesnet are ranked as the top two classifiers for financial risk datasets by this simplified approach and the other top M CDM methods.

Toward integrating feature selection algorithms for classification and clustering

  • Huan LiuLei Yu
  • Computer Science
    IEEE Transactions on Knowledge and Data Engineering
  • 2005
TLDR
With the categorizing framework, the efforts toward-building an integrated system for intelligent feature selection are continued, and an illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms.

An empirical study of classification algorithm evaluation for financial risk prediction

The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring

TLDR
A new hybrid mining approach in the design of an effective and appropriate credit scoring model based on genetic algorithm for credit scoring of bank customers in order to offer credit facilities to each class of customers.

A Study on Feature Selection Techniques in Educational Data Mining

TLDR
The result of the present study effectively supports the well known fact of increase in the predictive accuracy with the existence of minimum number of features and shows a reduction in computational time and constructional cost in both training and classification phases of the student performance model.

A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods

  • Chia-chi Sun
  • Computer Science, Business
    Expert Syst. Appl.
  • 2010

Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology

TLDR
This work introduces compound operators that dynamically change the topology of the search space to better utilize the information available from the evaluation of feature subsets and shows that compound operators unify previous approaches that deal with relevant and irrelevant features.

Supplier Selection in Uncertain Environment: A Fuzzy MCDM Approach

TLDR
A multi-criteria decision making (MCDM) method using Decision Making Trial and Evaluation Laboratory (DEMATEL) based on Analytic Network Process (ANP) with fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) to judiciously select suppliers based on important criteria and to point out interrelationships among dimensions and criteria in SCM by Network Relationship Map (NRM) for this company.