Pub Date : 2017-11-01DOI: 10.1109/ISKE.2017.8258793
Jian Zhong, Feng Cao, Guanfeng Wu, Yang Xu, Jun Liu
After extending the term "contradiction" from the traditionally defined a complementary pair based on two clauses into a typical unsatisfiable clause set (i.e., a standard contradiction which might consist of more than two clauses), a recent work by the same author group proposes a new inference principle and its sound and complete first-order theory framework to escape from the existing static binary resolution into a dynamic Multi-Clause Synergized Contradiction Separation based inference rule, which is essentially different from the multi-ary resolution, but includes binary resolution as its special case. The corresponding first-order automated deduction system is called MC-SCS. This present work focuses on the MC-SCS's reasoning algorithm scheme, proof procedure, implementation, and experimental results. The empirical evaluation shows promising results compared with some state of art first-order theorem provers.
{"title":"Multi-clause synergized contradiction separation based first-order theorem prover — MC-SCS","authors":"Jian Zhong, Feng Cao, Guanfeng Wu, Yang Xu, Jun Liu","doi":"10.1109/ISKE.2017.8258793","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258793","url":null,"abstract":"After extending the term \"contradiction\" from the traditionally defined a complementary pair based on two clauses into a typical unsatisfiable clause set (i.e., a standard contradiction which might consist of more than two clauses), a recent work by the same author group proposes a new inference principle and its sound and complete first-order theory framework to escape from the existing static binary resolution into a dynamic Multi-Clause Synergized Contradiction Separation based inference rule, which is essentially different from the multi-ary resolution, but includes binary resolution as its special case. The corresponding first-order automated deduction system is called MC-SCS. This present work focuses on the MC-SCS's reasoning algorithm scheme, proof procedure, implementation, and experimental results. The empirical evaluation shows promising results compared with some state of art first-order theorem provers.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114694445","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258746
Xuemin Yang, Zhifei Zhang, R. Yang, Daoyu Huang, Geng Yang, Lejun Gong
With the rapid growth of the high-throughput biological technology, it brings biomedical big omics' data containing literature and annotated data. Especially, a wealth of relevant information exists in various types of biomedical literature. Text mining has emerged as a potential solution to achieve knowledge for bridging between the free text and structured representation of biomedical information. In this work, we used deep learning to recognize biomedical entities. We obtained 84.0% precision, 69.5% recall, and 76.1% F-score aiming at the GENIA corpus, and obtained 91.3% precision, 91.1% recall, and 91.2% F-score aiming at the BioCreAtIvE II Gene Mention corpus. Experimental results show that our proposed approach is promising for developing biomedical text mining technology in biomedical entity recognition.
{"title":"Using deep learning to recognize biomedical entities","authors":"Xuemin Yang, Zhifei Zhang, R. Yang, Daoyu Huang, Geng Yang, Lejun Gong","doi":"10.1109/ISKE.2017.8258746","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258746","url":null,"abstract":"With the rapid growth of the high-throughput biological technology, it brings biomedical big omics' data containing literature and annotated data. Especially, a wealth of relevant information exists in various types of biomedical literature. Text mining has emerged as a potential solution to achieve knowledge for bridging between the free text and structured representation of biomedical information. In this work, we used deep learning to recognize biomedical entities. We obtained 84.0% precision, 69.5% recall, and 76.1% F-score aiming at the GENIA corpus, and obtained 91.3% precision, 91.1% recall, and 91.2% F-score aiming at the BioCreAtIvE II Gene Mention corpus. Experimental results show that our proposed approach is promising for developing biomedical text mining technology in biomedical entity recognition.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129694091","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258724
M. Xu, Yongchao Yin
Link prediction in networks is that using the existing known network structure or node information to predict the possibility between the two nodes which haven't connected to each other. It's important to learn about the evolution mechanism of network and the interaction relationship of nodes. The link possibility between nodes is closely related to the similarity. The method which is based on the node attributes and local information has the simple and direct calculation and better effect of prediction. So it is more suitable for the large-scale network applications. But it only considers the degree of final nodes or neighbor nodes and the number of neighbor nodes. Does not take into account that each neighbor nodes has the different effect for the different final nodes. The paper through experiments to analysis and compare different similarity contribution of neighbor nodes and end points. And further verified the weak-link effect in networks. Also we proposed a new common neighbor measurement algorithm, through distinguish the influence of each common neighbor for the different end nodes so that the prediction accuracy has been further improved.
{"title":"A similarity index algorithm for link prediction","authors":"M. Xu, Yongchao Yin","doi":"10.1109/ISKE.2017.8258724","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258724","url":null,"abstract":"Link prediction in networks is that using the existing known network structure or node information to predict the possibility between the two nodes which haven't connected to each other. It's important to learn about the evolution mechanism of network and the interaction relationship of nodes. The link possibility between nodes is closely related to the similarity. The method which is based on the node attributes and local information has the simple and direct calculation and better effect of prediction. So it is more suitable for the large-scale network applications. But it only considers the degree of final nodes or neighbor nodes and the number of neighbor nodes. Does not take into account that each neighbor nodes has the different effect for the different final nodes. The paper through experiments to analysis and compare different similarity contribution of neighbor nodes and end points. And further verified the weak-link effect in networks. Also we proposed a new common neighbor measurement algorithm, through distinguish the influence of each common neighbor for the different end nodes so that the prediction accuracy has been further improved.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"05 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130073542","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258747
Bin Xia, Yun Li, Qianmu Li, Tao Li
Due to the rapid development of Location-Based Social Networks (LBSNs), the Point of Interest (POI) recommendation has been attracted a lot of research attention. Based on the LBSNs, users are able to share their relevant visiting experience via check-in records. The sequential check-in data not only explicitly show users' moving trajectories, but also implicitly describe personal preferences and corresponding life patterns based on different contexts (e.g., time and geographical locations). The traditional POI recommender systems only consider common contexts (e.g., visit frequency, distance, and social relationship), but ignore the significance of life patterns for individuals during different time periods. In addition, current recommender systems hardly provide interpretable and explainable recommendations based on these limited contexts. In this paper, we propose an Attention-based Recurrent Neural Network (ARNN) to provide an explainable recommendation based on the sequential check-in data of the corresponding user. Our proposed approach makes use of the sequential check-in data to capture users' life pattern and utilizes a deep neural network to provide transparent recommendations. The major contribution of this paper are: (1) the proposed model is capable of providing explainable recommendations based on life patterns which implicitly describes the preference of the corresponding user; (2) the proposed approach is able to design a visiting plan (i.e., a series of recommendations) based on users' past visiting patterns instead of simply showing top-N recommendations; (3) we evaluate our proposed approach against a real world dataset and compare it to other start-of-the-art approaches.
{"title":"Attention-based recurrent neural network for location recommendation","authors":"Bin Xia, Yun Li, Qianmu Li, Tao Li","doi":"10.1109/ISKE.2017.8258747","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258747","url":null,"abstract":"Due to the rapid development of Location-Based Social Networks (LBSNs), the Point of Interest (POI) recommendation has been attracted a lot of research attention. Based on the LBSNs, users are able to share their relevant visiting experience via check-in records. The sequential check-in data not only explicitly show users' moving trajectories, but also implicitly describe personal preferences and corresponding life patterns based on different contexts (e.g., time and geographical locations). The traditional POI recommender systems only consider common contexts (e.g., visit frequency, distance, and social relationship), but ignore the significance of life patterns for individuals during different time periods. In addition, current recommender systems hardly provide interpretable and explainable recommendations based on these limited contexts. In this paper, we propose an Attention-based Recurrent Neural Network (ARNN) to provide an explainable recommendation based on the sequential check-in data of the corresponding user. Our proposed approach makes use of the sequential check-in data to capture users' life pattern and utilizes a deep neural network to provide transparent recommendations. The major contribution of this paper are: (1) the proposed model is capable of providing explainable recommendations based on life patterns which implicitly describes the preference of the corresponding user; (2) the proposed approach is able to design a visiting plan (i.e., a series of recommendations) based on users' past visiting patterns instead of simply showing top-N recommendations; (3) we evaluate our proposed approach against a real world dataset and compare it to other start-of-the-art approaches.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131980061","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258752
Gao-Jing Peng, Ke-Jia Chen, Shijun Xue, Bin Liu
This paper studies relation prediction in heterogeneous information networks under PU learning context. One of the challenges of this problem is the imbalance of data number between the positive set P (the set of node pairs with the target relation) and the unlabeled set U (the set of node pairs without the target relation). We propose a K-means and voting mechanism based technique SemiPUclus to extract the reliable negative set RN from U under a new relation prediction framework PURP. The experimental results show that PURP achieves better performance than comparative methods in DBLP co-authorship network data.
{"title":"A relation prediction method based on PU learning","authors":"Gao-Jing Peng, Ke-Jia Chen, Shijun Xue, Bin Liu","doi":"10.1109/ISKE.2017.8258752","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258752","url":null,"abstract":"This paper studies relation prediction in heterogeneous information networks under PU learning context. One of the challenges of this problem is the imbalance of data number between the positive set P (the set of node pairs with the target relation) and the unlabeled set U (the set of node pairs without the target relation). We propose a K-means and voting mechanism based technique SemiPUclus to extract the reliable negative set RN from U under a new relation prediction framework PURP. The experimental results show that PURP achieves better performance than comparative methods in DBLP co-authorship network data.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123116923","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258725
Gang Li, Zhenbo Li, Yongqiang Ren, Qingbo Yang, Huawen Liu
Since the introduction of uninorm in 1996, it has been widely used in many fields. In this paper, the class of uninorms of which the underlying operators are involutive and left-continuous is discussed. The structure of these classes of uninorms is described.
{"title":"On a class of uninorms of which the underlying operators are involutive and left-continuous","authors":"Gang Li, Zhenbo Li, Yongqiang Ren, Qingbo Yang, Huawen Liu","doi":"10.1109/ISKE.2017.8258725","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258725","url":null,"abstract":"Since the introduction of uninorm in 1996, it has been widely used in many fields. In this paper, the class of uninorms of which the underlying operators are involutive and left-continuous is discussed. The structure of these classes of uninorms is described.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120923474","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258775
Yating Shen, Yunyun Wang, Zhiguo Ma
Manifold regularization (MR) provides a powerful framework for semi-supervised classification, which propagates labels from the labeled instances to unlabeled ones so that similar instances over the manifold have similar classification outputs. However, labeled instances are randomly located. Label propagation from those instances to their neighbors may mislead the classification of MR. To address this issue, in this paper we develop a novel label-expanded MR framework (LE_MR for short) for semi-supervised classification. In LE_MR, a clustering strategy such as KFCM is first adopted to discover the high-confidence instances, i.e., instances in the central region of clusters. Then those instances along with the cluster indices are adopted to expand the labeled instances set. Experiments show that LE_MR obtains encouraging results compared to state-of-the-art semi-supervised classification methods.
{"title":"Label-expanded manifold regularization for semi-supervised classification","authors":"Yating Shen, Yunyun Wang, Zhiguo Ma","doi":"10.1109/ISKE.2017.8258775","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258775","url":null,"abstract":"Manifold regularization (MR) provides a powerful framework for semi-supervised classification, which propagates labels from the labeled instances to unlabeled ones so that similar instances over the manifold have similar classification outputs. However, labeled instances are randomly located. Label propagation from those instances to their neighbors may mislead the classification of MR. To address this issue, in this paper we develop a novel label-expanded MR framework (LE_MR for short) for semi-supervised classification. In LE_MR, a clustering strategy such as KFCM is first adopted to discover the high-confidence instances, i.e., instances in the central region of clusters. Then those instances along with the cluster indices are adopted to expand the labeled instances set. Experiments show that LE_MR obtains encouraging results compared to state-of-the-art semi-supervised classification methods.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116740040","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258716
Jun Huang, Weiqing Cheng
As online advertisements are increasing in number, many ad filtering tools have emerged, among which the most widely-used are AdBlock and AdBlock Plus. To use these tools effectively is of significance to the network users. First, this paper analyzes the filtering performance of both AdBlock and AdBlock Plus, using the percentage of ad requests blocked when accessing a web page in the total number of page requests, as well as web page loading time as two specific metrics for comparison. According to the results, AdBlock surpasses AdBlock Plus in terms of ad filtering capability, and the difference between those two tools is mainly due to different default filter lists they use. Then taking video and game filtering as an example, this paper explores how to use ad filtering tools to achieve traffic filtering with specific requirements on the basis of ad filtering principle and rule grammar. In such way, ad filtering tools are expanded to web traffic filtering in specific application scenarios.
{"title":"Filtering performance analysis and application study of advertising filtering tools","authors":"Jun Huang, Weiqing Cheng","doi":"10.1109/ISKE.2017.8258716","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258716","url":null,"abstract":"As online advertisements are increasing in number, many ad filtering tools have emerged, among which the most widely-used are AdBlock and AdBlock Plus. To use these tools effectively is of significance to the network users. First, this paper analyzes the filtering performance of both AdBlock and AdBlock Plus, using the percentage of ad requests blocked when accessing a web page in the total number of page requests, as well as web page loading time as two specific metrics for comparison. According to the results, AdBlock surpasses AdBlock Plus in terms of ad filtering capability, and the difference between those two tools is mainly due to different default filter lists they use. Then taking video and game filtering as an example, this paper explores how to use ad filtering tools to achieve traffic filtering with specific requirements on the basis of ad filtering principle and rule grammar. In such way, ad filtering tools are expanded to web traffic filtering in specific application scenarios.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123746755","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258843
Yixiong Bian, Huakang Li
With the rapid development of the internet, applications of recommendation systems for online shops and entertainment platforms become more and more popular. In order to improve the effectiveness of recommendation, external information has been incorporated into various algorithms, such as location and social relationship. However, most algorithms only focus on the introduction of external information without depth analysis of the intrinsic mechanism in the external information. This paper proposed a transfer model of social trusted relationship, and optimized the reliability of the transfer model using pruning algorithm based on original trust recommendation. A credible social relationship macro-transfer model based on iterations of new credible relationships is defined by the similarity of social relationships. With a certain interest topic as a source of information, a micro-transfer model achieves the theme of interest and credibility of the expansion using social information dissemination algorithm. To demonstrate the effectiveness of the macro and micro credible transfer models, we used the Mantra search tree pruning algorithm and the optimization algorithm of similar category replacing similar products. The experimental results show that the proposed method based on the macroscopic and microscopic transfer models of the trusted relationship enhances the success rate and stability of the recommended system.
{"title":"Recommendation system based on trusted relation transmission","authors":"Yixiong Bian, Huakang Li","doi":"10.1109/ISKE.2017.8258843","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258843","url":null,"abstract":"With the rapid development of the internet, applications of recommendation systems for online shops and entertainment platforms become more and more popular. In order to improve the effectiveness of recommendation, external information has been incorporated into various algorithms, such as location and social relationship. However, most algorithms only focus on the introduction of external information without depth analysis of the intrinsic mechanism in the external information. This paper proposed a transfer model of social trusted relationship, and optimized the reliability of the transfer model using pruning algorithm based on original trust recommendation. A credible social relationship macro-transfer model based on iterations of new credible relationships is defined by the similarity of social relationships. With a certain interest topic as a source of information, a micro-transfer model achieves the theme of interest and credibility of the expansion using social information dissemination algorithm. To demonstrate the effectiveness of the macro and micro credible transfer models, we used the Mantra search tree pruning algorithm and the optimization algorithm of similar category replacing similar products. The experimental results show that the proposed method based on the macroscopic and microscopic transfer models of the trusted relationship enhances the success rate and stability of the recommended system.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122806760","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 : 2017-11-01DOI: 10.1109/ISKE.2017.8258787
Hongdong Wang, Jia Meng, L. Zou, Siyuan Luo, Yuanyuan Shi
In order to solve the multi-attribute group decision-making problems, which the attribute weight is unknown and the index value of the alternative is linguistic-valued lattice implication algebra(LV(n×2)). This paper proposes a linguistic-valued lattice implication algebra TOPSIS method based on entropy weight method. We study the distance between the linguistic-valued on Lv(n×2) and their properties. Based to Lv(n×2) puts forward the Euclidean distance and weighted Euclidean distance get the similarity between the linguistic-valued on Lv(n×2). The weight of the attributes are determined according to the Lv(n×2) entropy method, and the alternatives are compared and ordered by TOPSIS method. The feasibility and validity of the method are verified by case analysis.
{"title":"Linguistic-valued lattice implication algebra TOPSIS method based on entropy weight method","authors":"Hongdong Wang, Jia Meng, L. Zou, Siyuan Luo, Yuanyuan Shi","doi":"10.1109/ISKE.2017.8258787","DOIUrl":"https://doi.org/10.1109/ISKE.2017.8258787","url":null,"abstract":"In order to solve the multi-attribute group decision-making problems, which the attribute weight is unknown and the index value of the alternative is linguistic-valued lattice implication algebra(LV(n×2)). This paper proposes a linguistic-valued lattice implication algebra TOPSIS method based on entropy weight method. We study the distance between the linguistic-valued on Lv(n×2) and their properties. Based to Lv(n×2) puts forward the Euclidean distance and weighted Euclidean distance get the similarity between the linguistic-valued on Lv(n×2). The weight of the attributes are determined according to the Lv(n×2) entropy method, and the alternatives are compared and ordered by TOPSIS method. The feasibility and validity of the method are verified by case analysis.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133472806","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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