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Wen-Bao Lin (2006)
A comparative study on the trends of entrepreneurial behaviors of enterprises in different strategies: Application of the social cognition theoryExpert Syst. Appl., 31
N. Krueger, Michael Reilly, A. Carsrud (2000)
Competing models of entrepreneurial intentionsJournal of Business Venturing, 15
A. Talukder, D. Casasent (2001)
A closed-form neural network for discriminatory feature extraction from high-dimensional dataNeural networks : the official journal of the International Neural Network Society, 14 9
C. Yeh, Der-Jang Chi, Ming-Fu Hsu (2010)
A hybrid approach of DEA, rough set and support vector machines for business failure predictionExpert Syst. Appl., 37
Donald Brown, V. Corruble, C. Pittard (1992)
A comparison of decision tree classifiers with backpropagation neural networks for multimodal classification problemsPattern Recognit., 26
D. Simon, J. Boring (1990)
Sensitivity, Specificity, and Predictive Value
E. Zanaty (2012)
Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classificationEgyptian Informatics Journal, 13
Tom Smith (2005)
Altruism and Empathy in America: Trends and Correlates
T. Masters (1995)
Advanced algorithms for neural networks: a C++ sourcebook
T. Masters (1995)
Advanced algorithms for neural networks
Joyce Nga, Gomathi Shamuganathan (2010)
The Influence of Personality Traits and Demographic Factors on Social Entrepreneurship Start Up IntentionsJournal of Business Ethics, 95
S. Haykin (1994)
Neural Networks: A Comprehensive Foundation
S. Kothari, H. Oh (1993)
Neural Networks for Pattern RecognitionAdv. Comput., 37
J. Carr, J. Sequeira (2007)
Prior family business exposure as intergenerational influence and entrepreneurial intent: A Theory of Planned Behavior approachJournal of Business Research, 60
L. Kolvereid, E. Isaksen (2006)
New business start-up and subsequent entry into self-employmentJournal of Business Venturing, 21
J. Min, Young-Chan Lee (2005)
Bankruptcy prediction using support vector machine with optimal choice of kernel function parametersExpert Syst. Appl., 28
E. Thompson (2009)
Individual Entrepreneurial Intent: Construct Clarification and Development of an Internationally Reliable MetricEntrepreneurship Theory and Practice, 33
(2009)
Entrepreneurial selfefficacy: Refining the measure and examining its relationship to attitudes toward venturing and nascent entrepreneurship
Y. Shao, R. Lunetta (2012)
Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data pointsIsprs Journal of Photogrammetry and Remote Sensing, 70
Hwanjo Yu, Jiong Yang, Jiawei Han (2003)
Classifying large data sets using SVMs with hierarchical clusters
C. John, N. Balakrishnan, J. Fiet (2000)
Modeling the relationship between corporate strategy and wealth creation using neural networksComput. Oper. Res., 27
S. Hong (1997)
Data miningFuture Gener. Comput. Syst., 13
M. Paliwal, U. Kumar (2009)
Neural networks and statistical techniques: A review of applicationsExpert Syst. Appl., 36
F. Questier, R. Put, D. Coomans, B. Walczak, Y. Heyden (2005)
The use of CART and multivariate regression trees for supervised and unsupervised feature selectionChemometrics and Intelligent Laboratory Systems, 76
C. Kuzey, Ali Uyar, D. Delen (2014)
The impact of multinationality on firm value: A comparative analysis of machine learning techniquesDecis. Support Syst., 59
N. Krueger (2000)
The Cognitive Infrastructure of Opportunity EmergenceEntrepreneurship Theory and Practice, 24
M. Behzad, K. Asghari, M. Eazi, M. Palhang (2009)
Generalization performance of support vector machines and neural networks in runoff modelingExpert Syst. Appl., 36
Yuhong Dai (2002)
Convergence Properties of the BFGS AlgoritmSIAM J. Optim., 13
Marcel Abendroth (2016)
Data Mining Practical Machine Learning Tools And Techniques With Java Implementations
C. Apté, S. Weiss (1997)
Data mining with decision trees and decision rulesFuture Gener. Comput. Syst., 13
A. Bolívar-Cimé, J. Marron (2013)
Comparison of binary discrimination methods for high dimension low sample size dataJ. Multivar. Anal., 115
M. Zekić-Sušac, Sanja Pfeifer, Ivana Đurđević (2010)
Classification of entrepreneurial intentions by neural networks, decision trees and support vector machinesCroatian Operational Research Review, 1
Sangjae Lee (2010)
Using data envelopment analysis and decision trees for efficiency analysis and recommendation of B2C controlsDecis. Support Syst., 49
J. Strossmayer (2006)
Modeling Small Business Credit Scoring by Using Logistic Regression , Neural Networks , and Decision Trees
M. Benšić, N. Šarlija, M. Zekić-Sušac (2005)
Modelling small-business credit scoring by using logistic regression, neural networks and decision treesIntell. Syst. Account. Finance Manag., 13
Guisong Liu, Zhang Yi, Shangming Yang (2007)
A hierarchical intrusion detection model based on the PCA neural networksNeurocomputing, 70
Hyun Shin, D. Eom, Sung-Shick Kim (2005)
One-class support vector machines - an application in machine fault detection and classificationComput. Ind. Eng., 48
H. Walker, W. Hall, J. Hurst (1976)
Clinical methods: The history, physical, and laboratory examinations
H. Triandis, M. Gelfand (1998)
Converging measurement of horizontal and vertical individualism and collectivismJournal of Personality and Social Psychology, 74
Steven Farmer, Xin Yao, Kate Kung-McIntyre (2011)
The Behavioral Impact of Entrepreneur Identity Aspiration and Prior Entrepreneurial ExperienceEntrepreneurship Theory and Practice, 35
M. Zekić-Sušac, N. Šarlija, Sanja Pfeifer (2013)
Combining PCA analysis and neural networks in modelling entrepreneurial intentions of studentsCroatian Operational Research Review, 4
Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.
Business Systems Research Journal – de Gruyter
Published: Sep 1, 2014
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