A particle-swarm-optimized fuzzy classifier used for investment decision support

We propose a Particle Swarm-based optimization technique to enhance the quality and performance of our Investment decision system (IDSS). It allows the classification of future performances of high-technology venture investments on the basis of very limited information. Our system thus helps investors to decide whether to invest in a young High-Technology Venture (HTV) or not. In order to cope with uncertain data we apply a Fuzzy Rule based Classifier. As we want to attain an objective and clear decision making process we implement a learning algorithm that learns rules from given real-world examples. The availability of data on early-stage investments is typically limited. For this reason we equipped our system with a bootstrapping mechanism which multiplies the number of examples without changing the quality. We show the efficacy of this approach as by comparing the classification power and other metrics of the PSO-optimized system with the respective characteristics of the conventionally built IDSS.