Random vector functional link forests and extreme learning forests applied to UAV automatic target recognition

This paper proposes two novel machine learning algorithms, namely Random Vector Functional Link Forests and Extreme Learning Forests, to develop an improved unmanned aerial vehicles automatic target recognition system. Such models take advantage of the stochastic procedure followed by Random Forests, where random subsets of instances and features are selected to build diverse Decision Trees. However, different from the usual uni-variate split criterion from Decision Tree algorithms, we propose and employ the novel Random Vector Functional Link Tree or Extreme Learning Tree, where each decision split is performed using the fast non-linear mapping of multiple features provided by either Random Vector Functional Link or Extreme Learning Machines. To prove the efficacy of the novel algorithm, experiments are performed using 90 binary classification problems to compare the performance of the proposed algorithm against other state-of-the-art ensemble learning techniques. Statistical analysis indicates the success of the proposed algorithms in terms of both predictive performance and computational complexity. While the model with deeper trees outperforms classical ensembles in terms of predictive performance (1.41% error reduction) and has similar results to state-of-the-art ensemble models, the model with shallow trees outperforms all ensembles in terms of computational burden (at least 36% faster). Finally, the novel methods are applied to develop an automatic target recognition system for unmanned aerial vehicles, achieving a valuable trade-off in terms of area under receiver operating characteristic curve (0.9309), F1 score (0.8190), accuracy (0.8646), and computational time (4.14 s).

Automated summary

This paper proposes two novel machine learning algorithms to improve the automatic target recognition system for unmanned aerial vehicles. These models make use of the stochastic procedure of Random Forests and employ the novel Random Vector Functional Link Tree or Extreme Learning Tree for decision split. Experimental results show that the proposed algorithms outperform other state-of-the-art ensemble learning techniques in terms of predictive performance and computational complexity.