TY - GEN
T1 - Important complexity reduction of random forest in multi-classification problem
AU - Hassine, Kawther
AU - Erbad, Aiman
AU - Hamila, Ridha
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Algorithm complexity in machine learning problems has been a real concern especially with large-scaled systems. By increasing data dimensionality, a particular emphasis is placed on designing computationally efficient learning models. In this paper, we propose an approach to improve the complexity of a multi-classification learning problem in cloud networks. Based on the Random Forest algorithm and the highly dimensional UNSW-NB 15 dataset, a tuning of the algorithm is first performed to reduce the number of grown trees used during classification. Then, we apply an importance-based feature selection to optimize the number of predictors involved in the learning process. All of these optimizations, implemented with respect to the best performance recorded by our classifier, yield substantial improvement in terms of computational complexity both during training and prediction phases.
AB - Algorithm complexity in machine learning problems has been a real concern especially with large-scaled systems. By increasing data dimensionality, a particular emphasis is placed on designing computationally efficient learning models. In this paper, we propose an approach to improve the complexity of a multi-classification learning problem in cloud networks. Based on the Random Forest algorithm and the highly dimensional UNSW-NB 15 dataset, a tuning of the algorithm is first performed to reduce the number of grown trees used during classification. Then, we apply an importance-based feature selection to optimize the number of predictors involved in the learning process. All of these optimizations, implemented with respect to the best performance recorded by our classifier, yield substantial improvement in terms of computational complexity both during training and prediction phases.
KW - Algorithm complexity
KW - Feature selection
KW - Predictor importance
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85073899562&partnerID=8YFLogxK
U2 - 10.1109/IWCMC.2019.8766544
DO - 10.1109/IWCMC.2019.8766544
M3 - Conference contribution
AN - SCOPUS:85073899562
T3 - 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
SP - 226
EP - 231
BT - 2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019
Y2 - 24 June 2019 through 28 June 2019
ER -