Pub Date : 1900-01-01DOI: 10.1109/GRC.2014.6982852
Xiaolong Tao, Qiaoran Wang, Juncheng Hu
In the era of knowledge-driven economy, self-management is becoming the main way of management. The precondition of carrying on self-management scientifically and effectively is to know the theory of self-management well and precisely. Many scholars are paying attention to the study of self-management. However, the research on dimensional structure of employee's self-management is limited. And the definition of self-management is vague. Through literature review and qualitative analysis, the thesis concludes the dimensional structure of employee's self-management, and then the conclusion will be examined through confirmatory factor analysis. It proves that employee's self-management consists of five dimensions, including self-knowledge, self-planning, self-control, self-learning and self-interpersonal management. It also verifies the practicability of the proposed framework.
{"title":"The empirical research on the dimensional structure of employee's self-management","authors":"Xiaolong Tao, Qiaoran Wang, Juncheng Hu","doi":"10.1109/GRC.2014.6982852","DOIUrl":"https://doi.org/10.1109/GRC.2014.6982852","url":null,"abstract":"In the era of knowledge-driven economy, self-management is becoming the main way of management. The precondition of carrying on self-management scientifically and effectively is to know the theory of self-management well and precisely. Many scholars are paying attention to the study of self-management. However, the research on dimensional structure of employee's self-management is limited. And the definition of self-management is vague. Through literature review and qualitative analysis, the thesis concludes the dimensional structure of employee's self-management, and then the conclusion will be examined through confirmatory factor analysis. It proves that employee's self-management consists of five dimensions, including self-knowledge, self-planning, self-control, self-learning and self-interpersonal management. It also verifies the practicability of the proposed framework.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116084476","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 : 1900-01-01DOI: 10.1109/GRC.2006.1635812
Lijuan Zhang, Zhou-Jun Li
Rough set theory has been widely and successfully used in data mining, especially in classification field. But most existing rough set based classification approaches require computing optimal attribute reduction, which is usually intractable and many problems related to it have been shown to be NP-hard. Although approximate algorithms exist, they also tend to be computationally expensive. This paper presents a novel rough set method for classification, which does not require computing attribute reduction. It stepwise investigates condition attributes and outputs the classification rules induced by them, which is just like the strategy of "on the fly". The theoretical analysis and the empirical study show that the proposed method is effective and efficient. Index Terms—rough set, attribute reduction, data mining, classification
{"title":"A novel rough set approach for classification","authors":"Lijuan Zhang, Zhou-Jun Li","doi":"10.1109/GRC.2006.1635812","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635812","url":null,"abstract":"Rough set theory has been widely and successfully used in data mining, especially in classification field. But most existing rough set based classification approaches require computing optimal attribute reduction, which is usually intractable and many problems related to it have been shown to be NP-hard. Although approximate algorithms exist, they also tend to be computationally expensive. This paper presents a novel rough set method for classification, which does not require computing attribute reduction. It stepwise investigates condition attributes and outputs the classification rules induced by them, which is just like the strategy of \"on the fly\". The theoretical analysis and the empirical study show that the proposed method is effective and efficient. Index Terms—rough set, attribute reduction, data mining, classification","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127201567","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 : 1900-01-01DOI: 10.1109/GRC.2006.1635865
Xiaohong Wu, Qunxia Wang
This paper presents a novel method to evaluate the quality of dissertations of undergraduates majoring in nature science. It adopts the method of rough set attributes reduction, applies the solving algorithm using attribute cores with the form of increasing the number of attributes, which results in a serial of rules and obtains a sound effect. Experiments illustrate the effectiveness of this algorithm.
{"title":"Application of rough set attributes reduction in quality evaluation of dissertation","authors":"Xiaohong Wu, Qunxia Wang","doi":"10.1109/GRC.2006.1635865","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635865","url":null,"abstract":"This paper presents a novel method to evaluate the quality of dissertations of undergraduates majoring in nature science. It adopts the method of rough set attributes reduction, applies the solving algorithm using attribute cores with the form of increasing the number of attributes, which results in a serial of rules and obtains a sound effect. Experiments illustrate the effectiveness of this algorithm.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121827090","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 : 1900-01-01DOI: 10.1109/GrC.2012.6468701
L. Yue, Jia-li Feng, Yongchang Liu
{"title":"Trend analysis for stock margin of safety on attribute theory","authors":"L. Yue, Jia-li Feng, Yongchang Liu","doi":"10.1109/GrC.2012.6468701","DOIUrl":"https://doi.org/10.1109/GrC.2012.6468701","url":null,"abstract":"","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116302366","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 : 1900-01-01DOI: 10.1109/GrC.2013.6740445
Hanning Yuan, Peng Wang
Now more and more heterogeneous handwritten digits data sets appear into sight. But traditional handwritten digits recognition algorithms are usually based on the homomorphism data sets. For solving the problem that handwritten digits data sets of different feature spaces can't compute, we constructed heterogeneous handwritten digits representation model based on multiple instance learning (MIL) where a bag contains handwritten digits data from different feature spaces. Handwritten digits classification algorithms (HB and HeterMIL) are designed and compared for handwritten digits recognition. Experiment results confirmed that the heterogeneous handwritten digits data representation model and recognition algorithms can solve the heterogeneous handwritten digits recognition effectively.
{"title":"Handwritten digits recognition using multiple instance learning","authors":"Hanning Yuan, Peng Wang","doi":"10.1109/GrC.2013.6740445","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740445","url":null,"abstract":"Now more and more heterogeneous handwritten digits data sets appear into sight. But traditional handwritten digits recognition algorithms are usually based on the homomorphism data sets. For solving the problem that handwritten digits data sets of different feature spaces can't compute, we constructed heterogeneous handwritten digits representation model based on multiple instance learning (MIL) where a bag contains handwritten digits data from different feature spaces. Handwritten digits classification algorithms (HB and HeterMIL) are designed and compared for handwritten digits recognition. Experiment results confirmed that the heterogeneous handwritten digits data representation model and recognition algorithms can solve the heterogeneous handwritten digits recognition effectively.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121751792","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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