Multilayer feedforward networks are universal approximators
Neural Networks
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Machine learning: neural networks, genetic algorithms, and fuzzy systems
Machine learning: neural networks, genetic algorithms, and fuzzy systems
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
An introduction to variable and feature selection
The Journal of Machine Learning Research
Optimization-based feature selection with adaptive instance sampling
Computers and Operations Research
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Feature selection in bankruptcy prediction
Knowledge-Based Systems
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
Computational Statistics & Data Analysis
Variable selection by association rules for customer churn prediction of multimedia on demand
Expert Systems with Applications: An International Journal
Building credit scoring models using genetic programming
Expert Systems with Applications: An International Journal
Failure prediction with self organizing maps
Expert Systems with Applications: An International Journal
A rough set approach to feature selection based on power set tree
Knowledge-Based Systems
Genetic algorithms in feature and instance selection
Knowledge-Based Systems
The impact of multinationality on firm value: A comparative analysis of machine learning techniques
Decision Support Systems
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It is very important for investors and creditors to understand the critical factors affecting a firm's value before making decisions about investments and loans. Since the knowledge-based economy has evolved, the method for creating firm value has transferred from traditional physical assets to intangible knowledge. Therefore, valuation of intangible assets has become a widespread topic of interest in the future of the economy. This study takes advantage of feature selection, an important data-preprocessing step in data mining, to identify important and representative factors affecting intangible assets. Particularly, five feature selection methods are considered, which include principal component analysis (PCA), stepwise regression (STEPWISE), decision trees (DT), association rules (AR), and genetic algorithms (GA). In addition, multi-layer perceptron (MLP) neural networks are used as the prediction model in order to understand which features selected from these five methods can allow the prediction model to perform best. Based on the chosen dataset containing 61 variables, the experimental result shows that combining the results from multiple feature selection methods performs the best. GA@?STEPWISE, DT@?PCA, and the DT single feature selection method generate approximately 75% prediction accuracy, which select 26, 22, and 7 variables respectively.