Correlation Based Multi-Criteria Feature Weighting for Identification of Top-k Investors and Companies

Network data analysis for Financial and Managerial applications may influence many business and IT activities. Such analysis can be useful to identify top Investors and Companies based on different criteria. Tradition approaches such as Centrality measures used in identifying top Investors and Companies. However, these approaches require high computation complexity. CrunchBase, a data platform, provides valuable information about Investors and Companies that helps to understand the behavior of peoples, their interest and can predict future investments using the graph-based tools like Neo4j. This research aims to use CrunchBase dataset and Multi-Criteria Decision Making (MCDM) approaches i.e. TOPSIS and Majority Voting to identify top-k Investor and Companies. In addition, proposed an efficient approach using Correlation-based Feature Weighting (CFW) for MCDM approaches. Experimental results show that proposed approaches provide scalability and perform better in terms of accuracy compared to state of-the-art approaches.