“Dimension Reduction: Feature Subset” Method for Selecting the Best Index Combination in Reputation Evaluation of Crowdsourcing Participants

Abstract

An effective reputation evaluation mechanism is an essential guarantee for the crowdsourcing mode's healthy, orderly, and rapid development. Aiming at the problems of unsound reputation evaluation mechanism, single reputation evaluation index, and poor discrimination ability of crowdsourcing platforms a “dimension reduction feature subset” method for selecting the best reputation evaluation index combination of crowdsourcing participants is proposed. This method first selects the best dimensionality reduction method by empirical method, then uses the classifier as the evaluation function of feature selection, and uses the sequential backward selection strategy (SBS) to select the feature subset and reputation evaluation algorithm with the best classification performance. The experimental results show that the reputation evaluation method of crowdsourcing participants based on ReliefF-SVM has the best performance in terms of accuracy, F1 measure, and stability and can select a comprehensive, objective, and effective evaluation index combination to distinguish the reputation status of crowdsourcing participants.