C4.5: programs for machine learning
C4.5: programs for machine learning
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
The nature of statistical learning theory
The nature of statistical learning theory
An illustration of verification and validation in the modelling phase of KBS development
Data & Knowledge Engineering
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Business Rules Applied: Building Better Systems Using the Business Rules Approach
Business Rules Applied: Building Better Systems Using the Business Rules Approach
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
IEEE Transactions on Software Engineering
Predicting going concern opinion with data mining
Decision Support Systems
Decompositional Rule Extraction from Support Vector Machines by Active Learning
IEEE Transactions on Knowledge and Data Engineering
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
| Hi-index | 12.05 |
Start-ups are crucial in the modern economy as they provide dynamism and growth. Research on the performance of new ventures increasingly investigates initial resources as determinants of success. Initial resources are said to be important because they imprint the firm at start-up, limit its strategic choices, and continue to impact its performance in the long run. The purpose of this paper is to identify configurations of initial resource bundles, strategy and environment that lead to superior performance in start-ups. To date, interdependencies between resources on the one hand and between resources, strategy and environment on the other hand have been neglected in empirical research. We rely on data mining for the analysis because it accounts for premises of configurational theory, including reversed causality, intradimensional interactions, multidimensional dependencies, and equifinality. We apply advanced data mining techniques, in the form of rule extraction from non-linear support vector machines, to induce accurate and comprehensible configurations of resource bundles, strategy and environment. We base our analysis on an extensive survey among 218 Flemish start-ups. Our experiments indicate the good performance of rule extraction technique ALBA. Finally, for comprehensibility, intuitiveness and implementation reasons, the tree is transformed into a decision table.