Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949192
C. Teng, Ben-Jian Dong
Image feature matching is a very important and fundamental task in computer vision. In this paper, a spatial-order based progressive feature matching framework is proposed. With the model of spatial order, the searching space is partitioned into many intervals with each interval associated with a probability that a correct match is occurred in this interval. Using this information, many incorrect features could be filtered out and only the survived features are passed for subsequent matching. As the features are progressively matched, the model of spatial order is also progressively updated and the lengths of partitioned intervals are further shortened to filter out more features. To demonstrate the feasibility of proposed system, a series of experiments were conducted. A standard benchmark image data set was used to test the proposed system and the results showed that the proposed framework can indeed produce more efficient and accurate feature matching compared with traditional brute force technique.
{"title":"Using Feature Spatial Order in Progressive Image Feature Matching","authors":"C. Teng, Ben-Jian Dong","doi":"10.1109/ICMLC48188.2019.8949192","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949192","url":null,"abstract":"Image feature matching is a very important and fundamental task in computer vision. In this paper, a spatial-order based progressive feature matching framework is proposed. With the model of spatial order, the searching space is partitioned into many intervals with each interval associated with a probability that a correct match is occurred in this interval. Using this information, many incorrect features could be filtered out and only the survived features are passed for subsequent matching. As the features are progressively matched, the model of spatial order is also progressively updated and the lengths of partitioned intervals are further shortened to filter out more features. To demonstrate the feasibility of proposed system, a series of experiments were conducted. A standard benchmark image data set was used to test the proposed system and the results showed that the proposed framework can indeed produce more efficient and accurate feature matching compared with traditional brute force technique.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123183934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949190
T. Takeda
Most of our daily activities consist of standing, sitting, lying and walking. Above all, sitting behavior is said to account for more than half of the waking hours, and it can be said that it is directly connected to the quality of our lives. In this research, we propose a method to evaluate the user's posture from the pressure distribution measured by the cushion type seat pressure sensor. In the proposed method, a classifier based on fuzzy inference is created from pressure values obtained from 16 pressure sensors, and the difference in posture such as normal posture and humpback, and daily life operation such as reading and paperwork are classified. The experimental results show that identification is possible with an accuracy of about 87%.
{"title":"Posture Estimation Method Using Cushion Type Seat Pressure Sensor","authors":"T. Takeda","doi":"10.1109/ICMLC48188.2019.8949190","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949190","url":null,"abstract":"Most of our daily activities consist of standing, sitting, lying and walking. Above all, sitting behavior is said to account for more than half of the waking hours, and it can be said that it is directly connected to the quality of our lives. In this research, we propose a method to evaluate the user's posture from the pressure distribution measured by the cushion type seat pressure sensor. In the proposed method, a classifier based on fuzzy inference is created from pressure values obtained from 16 pressure sensors, and the difference in posture such as normal posture and humpback, and daily life operation such as reading and paperwork are classified. The experimental results show that identification is possible with an accuracy of about 87%.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114342876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949228
Sufang Zhang, Jun-Hai Zhai, Bo-Jun Xie, Yan Zhan, Xin Wang
Representation learning is the base and crucial for consequential tasks, such as classification, regression, and recognition. The goal of representation learning is to automatically learning good features with deep models. Multimodal representation learning is a special representation learning, which automatically learns good features from multiple modalities, and these modalities are not independent, there are correlations and associations among modalities. Furthermore, multimodal data are usually heterogeneous. Due to the characteristics, multimodal representation learning poses many difficulties: how to combine multimodal data from heterogeneous sources; how to jointly learning features from multimodal data; how to effectively describe the correlations and associations, etc. These difficulties triggered great interest of researchers along with the upsurge of deep learning, many deep multimodal learning methods have been proposed by different researchers. In this paper, we present an overview of deep multimodal learning, especially the approaches proposed within the last decades. We provide potential readers with advances, trends and challenges, which can be very helpful to researchers in the field of machine, especially for the ones engaging in the study of multimodal deep machine learning.
{"title":"Multimodal Representation Learning: Advances, Trends and Challenges","authors":"Sufang Zhang, Jun-Hai Zhai, Bo-Jun Xie, Yan Zhan, Xin Wang","doi":"10.1109/ICMLC48188.2019.8949228","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949228","url":null,"abstract":"Representation learning is the base and crucial for consequential tasks, such as classification, regression, and recognition. The goal of representation learning is to automatically learning good features with deep models. Multimodal representation learning is a special representation learning, which automatically learns good features from multiple modalities, and these modalities are not independent, there are correlations and associations among modalities. Furthermore, multimodal data are usually heterogeneous. Due to the characteristics, multimodal representation learning poses many difficulties: how to combine multimodal data from heterogeneous sources; how to jointly learning features from multimodal data; how to effectively describe the correlations and associations, etc. These difficulties triggered great interest of researchers along with the upsurge of deep learning, many deep multimodal learning methods have been proposed by different researchers. In this paper, we present an overview of deep multimodal learning, especially the approaches proposed within the last decades. We provide potential readers with advances, trends and challenges, which can be very helpful to researchers in the field of machine, especially for the ones engaging in the study of multimodal deep machine learning.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134044045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949193
Ke-Shiuan Lynn, Chun-Ju Chen, C. Tseng, M. Cheng, Wen-Harn Pan
Liquid chromatography/mass spectrometer (LC/MS) has become one of the most popular analytical platform for metabolomics studies owing to its wide range of detectable polarity and molecular mass. However, metabolite identification remains quite costly and time-consuming in LC/MS-based metabolomics, mostly due to lower database integrity and a separated MS/MS spectra generation process. In this work, we constructed an automated, user-friendly, and freely available tool. From a peak list, the tool first groups peaks, which are usually associated with a metabolite, based on their retention time and abundance correlation across samples. In each group, different ions are annotated and the mass of the underlying metabolite is derived. Finally, the fragments are used to match with low-energy MS/MS spectra in public databases for metabolite identification. To identify metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. Through the above approach, we anticipate facilitating the metabolite identification in LC-MS-based metabolomics studies.
{"title":"An Automated Identification Tool for LC-MS Based Metabolomics Studies","authors":"Ke-Shiuan Lynn, Chun-Ju Chen, C. Tseng, M. Cheng, Wen-Harn Pan","doi":"10.1109/ICMLC48188.2019.8949193","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949193","url":null,"abstract":"Liquid chromatography/mass spectrometer (LC/MS) has become one of the most popular analytical platform for metabolomics studies owing to its wide range of detectable polarity and molecular mass. However, metabolite identification remains quite costly and time-consuming in LC/MS-based metabolomics, mostly due to lower database integrity and a separated MS/MS spectra generation process. In this work, we constructed an automated, user-friendly, and freely available tool. From a peak list, the tool first groups peaks, which are usually associated with a metabolite, based on their retention time and abundance correlation across samples. In each group, different ions are annotated and the mass of the underlying metabolite is derived. Finally, the fragments are used to match with low-energy MS/MS spectra in public databases for metabolite identification. To identify metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. Through the above approach, we anticipate facilitating the metabolite identification in LC-MS-based metabolomics studies.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126396501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949242
Kai-Wei Chen, M. Tsai
The additive manufacturing is an intelligent manufacturing technology that can quickly build a variety of complex objects with single or different functional materials. If additive manufacturing technology can be used to print mechanical structure with sensing or electronic feature, it will be able to break through the development bottleneck of a smart gripper and achieve the goal of rapid industrial development. In this study, a multi-nozzle pneumatic extrusion additive manufacturing system for printing soft and hard material structure was developed. The structure is made of a multi-material polymer which can be fabricated by using 3D printing machine. The liquid material is extruded through a tiny nozzle and then cured by a UV lighting source. The system architecture includes a CNC controller, which controls the nozzle through two stepping motors, both positive and negative pressures and curing light source are also manipulated with peripheral I/Os. A DA controller is also applied to flexibly control the air pressure for requirement of different injected flow speed. The program part is automatically executed with a numerical control software in CNC and PLC. Different pressures were set for extrusion nozzles with different materials. The G-code data was processed by Python Language and sent to the multi-nozzle pneumatic extrusion additive manufacturing system. This paper successfully printed a sandwich pad with soft and hard material structure, including double-layer material pad and three-layer material pad. A finer printing performance than a traditional FDM machine is achieved.
{"title":"Multi-Nozzle Pneumatic Extrusion Based Additive Manufacturing System for Fabricating a Sandwich Structure with Soft and Hard Material","authors":"Kai-Wei Chen, M. Tsai","doi":"10.1109/ICMLC48188.2019.8949242","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949242","url":null,"abstract":"The additive manufacturing is an intelligent manufacturing technology that can quickly build a variety of complex objects with single or different functional materials. If additive manufacturing technology can be used to print mechanical structure with sensing or electronic feature, it will be able to break through the development bottleneck of a smart gripper and achieve the goal of rapid industrial development. In this study, a multi-nozzle pneumatic extrusion additive manufacturing system for printing soft and hard material structure was developed. The structure is made of a multi-material polymer which can be fabricated by using 3D printing machine. The liquid material is extruded through a tiny nozzle and then cured by a UV lighting source. The system architecture includes a CNC controller, which controls the nozzle through two stepping motors, both positive and negative pressures and curing light source are also manipulated with peripheral I/Os. A DA controller is also applied to flexibly control the air pressure for requirement of different injected flow speed. The program part is automatically executed with a numerical control software in CNC and PLC. Different pressures were set for extrusion nozzles with different materials. The G-code data was processed by Python Language and sent to the multi-nozzle pneumatic extrusion additive manufacturing system. This paper successfully printed a sandwich pad with soft and hard material structure, including double-layer material pad and three-layer material pad. A finer printing performance than a traditional FDM machine is achieved.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131978695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949177
Han-Yun Chen, Ching-Hung Le, Baolian Huang
Because of the rise of environmental awareness, controlling and monitoring the electricity consumption become significant. The accuracy of the prediction of electricity consumption can directly influence the efficiency of power management. If the usage status of electricity can be predicted, it will be easy to discover if there is any unusual electricity consumption. The choice of suitable models or mathematic methods will be the essential of all. Adaptive network-based fuzzy inference system combines the concept of fuzzy and neural networks. It reserves the interpretability of fuzzy inference system and the learning ability of neural networks. We applied adaptive network-based fuzzy inference system (ANFIS) with hierarchical structure on electricity consumption prediction and grey relational analysis (GRA) on the influence of each input factors. The result showed that hierarchical ANFIS did achieve the purpose we set and GRA can effectively evaluate the magnitude of relation between factors and specific output.
{"title":"Electricity Consumption Forecasting of Buildings Using Hierarchical ANFIS and GRA","authors":"Han-Yun Chen, Ching-Hung Le, Baolian Huang","doi":"10.1109/ICMLC48188.2019.8949177","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949177","url":null,"abstract":"Because of the rise of environmental awareness, controlling and monitoring the electricity consumption become significant. The accuracy of the prediction of electricity consumption can directly influence the efficiency of power management. If the usage status of electricity can be predicted, it will be easy to discover if there is any unusual electricity consumption. The choice of suitable models or mathematic methods will be the essential of all. Adaptive network-based fuzzy inference system combines the concept of fuzzy and neural networks. It reserves the interpretability of fuzzy inference system and the learning ability of neural networks. We applied adaptive network-based fuzzy inference system (ANFIS) with hierarchical structure on electricity consumption prediction and grey relational analysis (GRA) on the influence of each input factors. The result showed that hierarchical ANFIS did achieve the purpose we set and GRA can effectively evaluate the magnitude of relation between factors and specific output.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131980691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949237
Shuai Shao, Jinseok Woo, Kouhei Yamamoto, N. Kubota
In recent years, the aging population has become a major social problem. We hope to achieve health-care system for older persons through technical means. In this study, we developed an elderly health care system based on vibration sensors. By analyzing the vibrations of behavior such as walking and falling, the system can determine the current state of the elderly and send it to the robot. Experiments show that our system can estimate the behavior of the elderly with an accuracy of 89%, in which the accuracy of fall detection is 96%.
{"title":"Elderly Health Care System Based on High Precision Vibration Sensor","authors":"Shuai Shao, Jinseok Woo, Kouhei Yamamoto, N. Kubota","doi":"10.1109/ICMLC48188.2019.8949237","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949237","url":null,"abstract":"In recent years, the aging population has become a major social problem. We hope to achieve health-care system for older persons through technical means. In this study, we developed an elderly health care system based on vibration sensors. By analyzing the vibrations of behavior such as walking and falling, the system can determine the current state of the elderly and send it to the robot. Experiments show that our system can estimate the behavior of the elderly with an accuracy of 89%, in which the accuracy of fall detection is 96%.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133879369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949314
An Fang
Question texts analysis is a challenging task of the fine-grained classification due to the few annotation data and unbalanced categories. The existing approaches normally assume that each word contributes the same semantic to the question text, but ignore the different meanings of the words and the dependency relations within the text. In this paper, we propose a deep neural network with multi-layer attention mechanism to capture the extended semantic features by using a dependency parsing tree, which has the capacity to spot the central components of the question. The experimental results demonstrate that our proposed model obtains substantially improvement, comparing with several competitive baselines.
{"title":"Short-Text Question Classification Based on Dependency Parsing and Attention Mechanism","authors":"An Fang","doi":"10.1109/ICMLC48188.2019.8949314","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949314","url":null,"abstract":"Question texts analysis is a challenging task of the fine-grained classification due to the few annotation data and unbalanced categories. The existing approaches normally assume that each word contributes the same semantic to the question text, but ignore the different meanings of the words and the dependency relations within the text. In this paper, we propose a deep neural network with multi-layer attention mechanism to capture the extended semantic features by using a dependency parsing tree, which has the capacity to spot the central components of the question. The experimental results demonstrate that our proposed model obtains substantially improvement, comparing with several competitive baselines.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131885615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we based on hybrid network of Dual-Radio Opportunistic Networking for Energy Efficiency (DRONEE) method and propose Dual-Radio Opportunistic Networking for Energy Efficiency using fuzzy logic control with multi-hop (DRONEE-FM) to improve original method which is a mixed network method using the cluster concept of a Wireless Sensor Network (WSN). Mobile phone users are divided into clusters and the best mobile phone user signal is selected as a cluster head in each cluster where that device is used to forward data to the base station. Other cluster members pass their transmission data to the cluster head through a Wi-Fi interface and the cluster head of nodes which does not communicate with the base station channels (i.e., 3G / 4G mobile networks, etc.) will be closed. Thus, signal interference from other mobile phone users affecting cluster head mobile phone users can be reduced and the channel quality can be improved.
{"title":"Performance Evaluation of a Mobile Deice System Using Fuzzy Logic Control with Multi-Hop in a Multi-Radio Opportunistic Network","authors":"Young-Long Chen, Neng-Chung Wang, Jing-Fong Ciou, Gun-Wen Xiao, Yi-Shang Liu, Pin-Lun Huang","doi":"10.1109/ICMLC48188.2019.8949293","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949293","url":null,"abstract":"In this paper, we based on hybrid network of Dual-Radio Opportunistic Networking for Energy Efficiency (DRONEE) method and propose Dual-Radio Opportunistic Networking for Energy Efficiency using fuzzy logic control with multi-hop (DRONEE-FM) to improve original method which is a mixed network method using the cluster concept of a Wireless Sensor Network (WSN). Mobile phone users are divided into clusters and the best mobile phone user signal is selected as a cluster head in each cluster where that device is used to forward data to the base station. Other cluster members pass their transmission data to the cluster head through a Wi-Fi interface and the cluster head of nodes which does not communicate with the base station channels (i.e., 3G / 4G mobile networks, etc.) will be closed. Thus, signal interference from other mobile phone users affecting cluster head mobile phone users can be reduced and the channel quality can be improved.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115914150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949182
Jintao Huang, Wenbin Qian, Binglong Wu, Yinglong Wang
Cost-sensitive feature selection is an important research topic in the field of machine learning and data mining. Presently, cost-sensitive feature selection research is mainly oriented to single-label or multi-label data. Since in many fields of application, there is a correlation and significance among the labels for multi-label data. In order to resolve the problems, this paper introduces label significance into cost-sensitive feature selection, and proposes a feature selection algorithm using test cost based on label significance. The algorithm combines the test cost matrix generated by the three distributions with positive region. Finally, the effectiveness and feasibility of the algorithm are further verified by experimental results on the four Mulan data set.
{"title":"Cost-Sensitive Feature Selection Based on Label Significance and Positive Region","authors":"Jintao Huang, Wenbin Qian, Binglong Wu, Yinglong Wang","doi":"10.1109/ICMLC48188.2019.8949182","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949182","url":null,"abstract":"Cost-sensitive feature selection is an important research topic in the field of machine learning and data mining. Presently, cost-sensitive feature selection research is mainly oriented to single-label or multi-label data. Since in many fields of application, there is a correlation and significance among the labels for multi-label data. In order to resolve the problems, this paper introduces label significance into cost-sensitive feature selection, and proposes a feature selection algorithm using test cost based on label significance. The algorithm combines the test cost matrix generated by the three distributions with positive region. Finally, the effectiveness and feasibility of the algorithm are further verified by experimental results on the four Mulan data set.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122661515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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