November 2022
·
5 Reads
·
2 Citations
This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.
November 2022
·
5 Reads
·
2 Citations
March 2021
·
26 Reads
·
14 Citations
The Review of Socionetwork Strategies
Personal identification is the task of authenticating a person using individual biological features. Deep neural networks (DNNs) have demonstrated an impressive performance in this field. Since no general algorithm is available for the design of network structures and the parameters adopted in DNNs for every application problem, DNNs should be determined according to the programmers’ experiments and know-how. For a new application task, it is very time-consuming for non-experts to design network structure, hyperparameters and an ensemble of base models adequately and effectively. In this paper, we present a genetic algorithm (GA)-based approach to construct network structures, tune their hyperparameters, and generate base models for the ensemble algorithm. The ensemble is constructed from base models with different network structures according to the voting ensemble algorithm. Our original personal identification dataset is employed as the numerical example to illustrate the performance of the proposed method. The results show that the prediction accuracy of the ensemble model is better than that of the base models and that the prediction of walking behavior toward the Kinect at 90 degrees and 225 degrees is more difficult than other walking behaviors.
November 2019
·
21 Reads
·
5 Citations
... Traditional ensemble methods [15,16] often overlook variations in predictive performance among different models, limiting the potential benefits of ensemble learning. Zhao et al. [17] evaluated the predictive performance of each base model through in-sample testing and calculated the weight of each model. They then used these weights to train the model for weight prediction, and finally, calculated a weighted average to obtain the ensemble result. ...
November 2022
... It's crucial to note that, in order to guarantee the predictive performance of the ensemble learning model, the selection of basic learners must consider both model accuracy as well as model diversity. GA [14,31] mimics the natural process of chromosomal recombination evolution and has been demonstrated to be wellsuited for optimization problems related to genomics. To streamline and automate this process, we utilize the "GA" package to optimize the selected basic learners. ...
March 2021
The Review of Socionetwork Strategies
... Many researchers have validated the effectiveness of optimization algorithms in deep learning model structure and key parameter design [14][15][16]. Feng et al. [17] verified that the ensembled depth model optimized by the optimization algorithm has better performance than the single model. Doaa [18] proposed a usage prediction method based on a weighted ensemble of machine learning models, which uses example swarm optimization guided whale optimization algorithm to optimize the weights of the base model. ...
November 2019