• Title/Summary/Keyword: Ranking

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Applying a modified AUC to gene ranking

  • Yu, Wenbao;Chang, Yuan-Chin Ivan;Park, Eunsik
    • Communications for Statistical Applications and Methods
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    • v.25 no.3
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    • pp.307-319
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    • 2018
  • High-throughput technologies enable the simultaneous evaluation of thousands of genes that could discriminate different subclasses of complex diseases. Ranking genes according to differential expression is an important screening step for follow-up analysis. Many statistical measures have been proposed for this purpose. A good ranked list should provide a stable rank (at least for top-ranked gene), and the top ranked genes should have a high power in differentiating different disease status. However, there is a lack of emphasis in the literature on ranking genes based on these two criteria simultaneously. To achieve the above two criteria simultaneously, we proposed to apply a previously reported metric, the modified area under the receiver operating characteristic cure, to gene ranking. The proposed ranking method is found to be promising in leading to a stable ranking list and good prediction performances of top ranked genes. The findings are illustrated through studies on both synthesized data and real microarray gene expression data. The proposed method is recommended for ranking genes or other biomarkers for high-dimensional omics studies.

Ranking of Websites of State Universities in Tamil Nadu using WISER and PRIMOEX: An Analytical Study

  • Dhanavandan, S.;Varadharajalu, J.
    • International Journal of Knowledge Content Development & Technology
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    • v.9 no.3
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    • pp.7-22
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    • 2019
  • This Study examines and explores through a web metric ranking analysis of the websites of 22 state universities in Tamil Nadu. It discussed the domain systems of the websites; WISER Web ranking for the year 2016 and 2017. Similarly, the researchers manipulated a new ranking tools for ranking the websites of State universities of Tamil Nadu, According to WISER ranking, Anna University is ranked first with 2469.4 WISER value followed by Tamil Nadu Agricultural University which is ranked second with 4031.4 WISER value. University of Madras is ranked third with 4333.8 WISER values in the year 2017. But when we used the PRIMOEX ranking, Anna University is ranked first with 3653.3 PRIMOEX value followed by Tamil Nadu Agricultural University which is ranked second with 4090.6 PRIMOEX value. Madurai Kamaraj University is ranked third with 4136.1 PRIMOEX values. Alagappa University is ranked fourth with 4956.1 PRIMOEX value and Alagappa University with 6001.9 PRIMOEX value is ranked fifth. It found that PRIMOEX good tool to measure the ranking of websites of state universities of Tamil Nadu.

Ordering Items from Ranking Procedures in Survey Research (조사연구에서 순위절차를 이용한 항목순위결정에 관한 연구)

  • Heo, Sun-Yeong;Chang, Duk-Joon;Shin, Jae-Kyoung
    • Survey Research
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    • v.9 no.2
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    • pp.29-49
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    • 2008
  • Many survey data are collected today to measure personal values and to order them according to their importance. There are two popular procedures to achieve the goal: ranking procedures and rating procedures. The ranking procedures can be divided into two categories; full ranking procedures and reduced ranking procedures. The reduced ranking procedure is more often used because of its easiness to respondents. However, the ordered responses are not generally incorporated into ordering their values. This research has studied ways to incorporate the ordered responses into ordering the values. We have considered the ranking scales as the conditional rating scales. Our findings are that the ordering values based on the weighted proportions is better than one based on the unweighted proportions.

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A Study on Fuzzy Ranking Model based on User Preference (사용자 선호도 기반의 퍼지 랭킹모델에 관한 연구)

  • Kim Dae-Won
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2006.05a
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    • pp.94-95
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    • 2006
  • A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. In this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.

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A Study on Fuzzy Ranking Model based on User Preference

  • Kim Dae-Won
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.3
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    • pp.326-331
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    • 2006
  • A great deal of research has been made to model the vagueness and uncertainty in information retrieval. One such research is fuzzy ranking models, which have been showing their superior performance in handling the uncertainty involved in the retrieval process. In this study we develop a new fuzzy ranking model based on the user preference. Through the experiments on the TREC-2 collection of Wall Street Journal documents, we show that the proposed method outperforms the conventional fuzzy ranking models.

A Ranking Method for Fuzzy Numbers based on Fuzzy Comparisons (퍼지 비교 기반 퍼지 숫자의 등급과 방법)

  • Lee, Jee-Hyong;Lee, Kwang-Hyung
    • Journal of KIISE:Software and Applications
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    • v.28 no.12
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    • pp.930-937
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    • 2001
  • For ranking fuzzy numbers, comparisons between numbers are necessary However, the comparison results can be vague since fuzzy numbers represent vague numeric values. Thus, ranking results of fuzzy numbers which are based on comparisons between fuzzy numbers, could also be vague. This means that there could be several possible ranking sequences of fuzzy numbers. There have been proposed many ranking methods for fuzzy numbers. However, most of them generate only ranking sequence. In this paper, we present a ranking method for fuzzy numbers using the fuzzy satisfaction function, Our method generates several possible ranking sequences of the given fuzzy numbers using the fuzzy satisfaction function.

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Tightly Coupled Integration of Ranking SVM and RDBMS (랭킹 SVM과 RDBMS의 밀결합 통합)

  • Song, Jae-Hwan;Oh, Jin-Oh;Yang, Eun-Seok;Yu, Hwan-Jo
    • Journal of KIISE:Databases
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    • v.36 no.4
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    • pp.247-253
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    • 2009
  • Rank learning and processing have gained much attention in the IR and data mining communities for the last decade. While other data mining techniques such as classification and regression have been actively researched to interoperate with RDBMS by using the tightly coupled or loose coupling approaches, ranking has been researched independently without integrating into RDBMS. This paper proposes a tightly coupled integration of the Ranking SVM into MySQL in order to perform the rank learning task efficiently within the RDBMS. We implemented new SQL commands for learning ranking functions and predicting ranking scores. We evaluated our tightly coupled integration of Ranking SVM by comparing it to a loose coupling implementation. The experiment results show that our approach has a performance improvement of $10{\sim}40%$ in the training phase and 60% in the prediction phase.

An Estimated Closeness Centrality Ranking Algorithm and Its Performance Analysis in Large-Scale Workflow-supported Social Networks

  • Kim, Jawon;Ahn, Hyun;Park, Minjae;Kim, Sangguen;Kim, Kwanghoon Pio
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.3
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    • pp.1454-1466
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    • 2016
  • This paper implements an estimated ranking algorithm of closeness centrality measures in large-scale workflow-supported social networks. The traditional ranking algorithms for large-scale networks have suffered from the time complexity problem. The larger the network size is, the bigger dramatically the computation time becomes. To solve the problem on calculating ranks of closeness centrality measures in a large-scale workflow-supported social network, this paper takes an estimation-driven ranking approach, in which the ranking algorithm calculates the estimated closeness centrality measures by applying the approximation method, and then pick out a candidate set of top k actors based on their ranks of the estimated closeness centrality measures. Ultimately, the exact ranking result of the candidate set is obtained by the pure closeness centrality algorithm [1] computing the exact closeness centrality measures. The ranking algorithm of the estimation-driven ranking approach especially developed for workflow-supported social networks is named as RankCCWSSN (Rank Closeness Centrality Workflow-supported Social Network) algorithm. Based upon the algorithm, we conduct the performance evaluations, and compare the outcomes with the results from the pure algorithm. Additionally we extend the algorithm so as to be applied into weighted workflow-supported social networks that are represented by weighted matrices. After all, we confirmed that the time efficiency of the estimation-driven approach with our ranking algorithm is much higher (about 50% improvement) than the traditional approach.

A Folksonomy Ranking Framework: A Semantic Graph-based Approach (폭소노미 사이트를 위한 랭킹 프레임워크 설계: 시맨틱 그래프기반 접근)

  • Park, Hyun-Jung;Rho, Sang-Kyu
    • Asia pacific journal of information systems
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    • v.21 no.2
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    • pp.89-116
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    • 2011
  • In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.

What Gift and to Whom? : Choosing a Gift Based on Psychological Distance (누구에게? 어떤 선물을? : 선물 선택 시 심리적 거리를 중심으로)

  • Lee, Hyowon;Kang, Hyunmo
    • Knowledge Management Research
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    • v.22 no.2
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    • pp.95-117
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    • 2021
  • In this study, we investigate which alternatives to choose when giving a gift, according to the giver's relationship with the receiver. In particular, we study which alternatives are preferred when the prices are approximately the same: products with high-brand status but low-model ranking or products with low-brand status but high-model ranking. Leclerc, Hsee, and Nunes(2005) conceptualized the relative preference between a low-ranking model of a high-status brand and a high racking model of a low-status brand. The category effect is the preference for lower-ranking models of high-status brands. Meanwhile, the ranking effect refers to the preference for higher-ranking models of low-ranking brands. Based on construal level theory, the current study suggests that the category and ranking effects vary depending on the giver's relationship (vertical vs. horizontal) and intimacy (distant vs. close) with the person who will receive the gift. We manipulate the relationship and intimacy of the subject receiving the gift and verify the interaction effect. Results reveal that the giver exhibited a category effect in vertical relationships in which the psychological distance was far from the relationship. However, the ranking effect was found in horizontal relationships in which the psychological distance was close. Lastly, the gift selection significantly depends on the level. Overall, this study showed that when choosing a gift, the selection of a low-ranking model of a product from a high-tier brand or a high-ranking model from a low-tier brand might vary depending on the type of relationship and the level of intimacy. In addition, our findings provided managerial implications in targeting and marketing communication strategies based on product status.