An Intelligent Model for Success Prediction of Initial Coin Offerings

IEEE Access

Saad Mohamed Darwish¹  Ismail Ibrahim Gomaa²  Mohamed Gihan Ali² 

[1] Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, Alexandria, Egypt;Faculty of Commerce, Damanhour University, Damanhour, Egypt;

关键词: Initial coin offerings;    fuzzy support vector machine;    genetic algorithm;    imbalanced classification;    feature selection;   

DOI  :  10.1109/ACCESS.2022.3178369

来源: DOAJ

【 摘 要 】

Assessment of Initial Coin Offerings (ICOs) is crucial for investment decisions in the ICO market. Since most ICOs succeed in raising funds, failed ICOs must be discriminated against through intelligent classification methods. In this context, this research proposes an intelligent decision model for predicting ICOs’ success that merges both the Information Gain Directed Feature Selection (IGDFS) technique as a features rank procedure to select the discriminative features representing the initial pool of features for Genetic Algorithm (GA) to find the ICO’s optimal feature set and Fuzzy Support Vector Machine for Class Imbalance Learning (FSVM-CIL) to tackle the problem of imbalanced classification. Two benchmark datasets were used to examine the proposed hybrid model referred to as IGDFS-FSVM. The experimental results reveal that the proposed model that employs an intelligent technique for ICO’s feature selection outperforms state-of-the-art classifiers without features selection. In this regard, we conclude that the proposed hybrid model is a practical approach to support investment decisions in the ICO market.

【 授权许可】

Unknown