Abstract
In data mining and machine learning methods, it is traditionally assumed that training data, test data, and the data that will be processed in the future, should have the same feature space distribution. This is a condition that will not happen in the real world. In order to overcome this challenge, domain adaptation-based methods are used. One of the existing challenges in domain adaptation-based methods is to select the most efficient features so that they can also show the most efficiency in the destination database. In this paper, a new feature selection method based on deep reinforcement learning is proposed. In the proposed method, in order to select the best and most appropriate features, the essential policies in deep reinforcement learning are defined, and then the selection features are applied for training random forest, k-nearest neighborhood and support vector machine classifiers. The trained classifiers with the considered features are evaluated on the target database. The results are evaluated with the criteria of accuracy, sensitivity, positive and negative predictive rates in the classifiers. The achieved results show the superiority of the proposed method of feature selection when used in domain adaptation. By implementing the RF classifier on the VisDA-2018 database and the Syn2Real database, the classification accuracy in the feature selection of the proposed deep learning reinforcement has increased compared to the two-feature selection of Laplace monitoring and feature selection states. The classification sensitivity with the help of SVM classifier on the Syn2Real databases had the highest values in the feature selection state of the proposed deep learning reinforcement. The obtained number 100 is a positive predictive rate in the Syn2Real database with the help of SVM classifier and in the case of selecting the proposed feature, it indicates its superiority. The negative predictive rate in the Syn2Real database in the state of feature selection of the proposed deep reinforcement learning was 100%, which showed its superiority in comparison with 90.1% in the state of selecting the Laplace monitoring feature. Gmean in KNN classifier on the Syn2Real database has improved in the feature selection state of the proposed deep learning reinforcement in comparison to without feature selection state.
Highlights
In inter-domain and real-world problems, this condition is not met, and the classification model created in the training domain will have low accuracy in predicting the labels of the test domain samples
The positive predictive rate of the classification was evaluated with the help of three classifiers random forest (RF), KNN and support vector machine (SVM) on the two databases used in the article in three states of selecting the Laplace monitoring feature, in the feature selection of the proposed deep learning reinforcement and without feature selection
The negative prediction rate of the classification was evaluated with the help of three classifiers RF, KNN and SVM on the two databases used in the paper in three states of selecting the Laplace monitoring feature, selecting the proposed deep reinforcement learning feature and without the feature selection
Summary
Machine learning is one of the most widely used branches of artificial intelligence, which creates algorithms based on which systems can learn. In segmentation and classification cases, one domain has sufficient data, while in other domains perhaps there is no any data at all, or there may not be enough data, or the data attribute space is completely different In this case, if the methods based on domain adaptation or knowledge transfer perform properly, learning efficiency will be increased so well. Because of the existence of noisy, irrelevant and additional data, learning algorithms slow down significantly and reduce the efficiency of learning methods, which leads to difficulty in the model interpretation This challenge is evident in the field of adaptation learning. The main purpose of this research is to present a new method in feature selection based on deep reinforcement learning in domain adaptation. This is in the condition that they have a few training data [11]
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