Genetic Algorithm-based Variable Selection Approach for High-Growth Firm Prediction

• In this paper, we propose a novel method for high-growth firm prediction by minimizing a cost function using a Genetic Algorithm (GA). To achieve it, the GA is used to search to find a set of important variables which provide the best fit for machine learning models so that accurate predictions can be made for high-growth firm prediction. The GA is employed to optimize the mean square error (MSE) between the accurate results and the predicted results of the machine learning methods by evolving the initially generated binary solutions through iterations. The proposed method obtains the best fitting set of variables for the machine learning methods for high-growth firm prediction. Four different machine learning methods which are Support Vector Machines (SVM), Logistic Regression, Random Forest (RF) and K-Nearest Neighbor (K-NN) have been employed with the GA and experimental results show that using RF with the GA achieves the best accuracy results with 94.93%. © 2022 IEEE.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Computational complexity

Cost functions

Genetic algorithms

Learning algorithms

Learning systems

Mean square error

Nearest neighbor search

Support vector regression

Complexity

Cost-function

High growth

High-growth firm prediction

Machine learning methods

Machine-learning

Novel methods

Optimisations

Random forests

Variables selections

Forecasting

Genetic algorithm

machine learning

optimization

variable selection

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