{"title":"面向活动识别的pso学习人工神经网络","authors":"Raki Anwar Ekaniza, S. Suyanto","doi":"10.1109/ISRITI51436.2020.9315354","DOIUrl":null,"url":null,"abstract":"The purpose of Activity Recognition (AR) is to recognize human activity using a sensor to get the data needed. Then, a machine learning approach is used to determine the type of activity performed. A machine learning technique often used in the classification problem is Artificial Neural Network (ANN), which is trained using a backpropagation algorithm. Although this technique has been significantly developed, it still has a few disadvantages compared to others. One of the disadvantages of the ANN is that the result is not always optimum because of randomized initialization and epoch limit. In this paper, a Particle Swarm Optimization (PSO) is proposed to train the ANN. Some experiments on a dataset of 10 k activities with six imbalanced classes show that the PSO-based ANN produces effectiveness of 100% and an F1 score micro of 0.88, which are much higher than the back propagation-based ANN that gives the effectiveness of 75% and an F1 score micro of 0.87.","PeriodicalId":325920,"journal":{"name":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PSO-Learned Artificial Neural Networks for Activity Recognition\",\"authors\":\"Raki Anwar Ekaniza, S. Suyanto\",\"doi\":\"10.1109/ISRITI51436.2020.9315354\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of Activity Recognition (AR) is to recognize human activity using a sensor to get the data needed. Then, a machine learning approach is used to determine the type of activity performed. A machine learning technique often used in the classification problem is Artificial Neural Network (ANN), which is trained using a backpropagation algorithm. Although this technique has been significantly developed, it still has a few disadvantages compared to others. One of the disadvantages of the ANN is that the result is not always optimum because of randomized initialization and epoch limit. In this paper, a Particle Swarm Optimization (PSO) is proposed to train the ANN. Some experiments on a dataset of 10 k activities with six imbalanced classes show that the PSO-based ANN produces effectiveness of 100% and an F1 score micro of 0.88, which are much higher than the back propagation-based ANN that gives the effectiveness of 75% and an F1 score micro of 0.87.\",\"PeriodicalId\":325920,\"journal\":{\"name\":\"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISRITI51436.2020.9315354\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISRITI51436.2020.9315354","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PSO-Learned Artificial Neural Networks for Activity Recognition
The purpose of Activity Recognition (AR) is to recognize human activity using a sensor to get the data needed. Then, a machine learning approach is used to determine the type of activity performed. A machine learning technique often used in the classification problem is Artificial Neural Network (ANN), which is trained using a backpropagation algorithm. Although this technique has been significantly developed, it still has a few disadvantages compared to others. One of the disadvantages of the ANN is that the result is not always optimum because of randomized initialization and epoch limit. In this paper, a Particle Swarm Optimization (PSO) is proposed to train the ANN. Some experiments on a dataset of 10 k activities with six imbalanced classes show that the PSO-based ANN produces effectiveness of 100% and an F1 score micro of 0.88, which are much higher than the back propagation-based ANN that gives the effectiveness of 75% and an F1 score micro of 0.87.