Research initiative, treatment action : RITA最新文献_第5页

Unsupervised Feature Selection Methodology for Clustering in High Dimensionality Datasets 高维数据集聚类的无监督特征选择方法

Research initiative, treatment action : RITA Pub Date : 2020-04-27 DOI: 10.22456/2175-2745.96081

Marcos de Souza Oliveira, Sergio Queiroz

{"title":"Unsupervised Feature Selection Methodology for Clustering in High Dimensionality Datasets","authors":"Marcos de Souza Oliveira, Sergio Queiroz","doi":"10.22456/2175-2745.96081","DOIUrl":"https://doi.org/10.22456/2175-2745.96081","url":null,"abstract":"Feature selection is an important research area that seeks to eliminate unwanted features from datasets. Many feature selection methods are suggested in the literature, but the evaluation of the best set of features is usually performed using supervised metrics, where labels are required. In this work we propose a methodology that tries to aid data specialists to answer simple but important questions, such as: (1) do current feature selection methods give similar results? (2) is there is a consistently better method ? (3) how to select the m -best features? (4) as the methods are not parameter-free, how to choose the best parameters in the unsupervised scenario? and (5) given different options of selection, could we get better results if we fusion the results of the methods? If yes, how can we combine the results? We analyze these issues and propose a methodology that, based on some unsupervised methods, will make feature selection using strategies that turn the execution of the process fully automatic and unsupervised, in high-dimensional datasets. After, we evaluate the obtained results, when we see that they are better than those obtained by using the selection methods at standard configurations. In the end, we also list some further improvements that can be made in future works.","PeriodicalId":82472,"journal":{"name":"Research initiative, treatment action : RITA","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88187662","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}

引用次数: 4

Scrumie: Scrum Teaching Agent Oriented Game Scrum: Scrum教学代理导向游戏

Research initiative, treatment action : RITA Pub Date : 2020-04-27 DOI: 10.22456/2175-2745.98203

L. Marinho, Suelen Regina C. dos Santos, Leonardo Andrade, Bruna Costa Cons, Marcelo Schots, Vera Werneck

引用次数: 1

Effects of reward distribution strategies and perseverance profiles on agent-based coalitions dynamics 奖励分配策略和毅力对基于主体的联盟动力学的影响

Research initiative, treatment action : RITA Pub Date : 2020-04-27 DOI: 10.22456/2175-2745.94845

Luis Gustavo Ludescher, Jaime Simão Sichman

引用次数: 0

Prize Collecting Traveling Salesman Problem with Ridesharing 拼车的问题

Research initiative, treatment action : RITA Pub Date : 2020-04-27 DOI: 10.22456/2175-2745.94082

Ygor Alcântara de Medeiros, M. Goldbarg, E. Goldbarg

{"title":"Prize Collecting Traveling Salesman Problem with Ridesharing","authors":"Ygor Alcântara de Medeiros, M. Goldbarg, E. Goldbarg","doi":"10.22456/2175-2745.94082","DOIUrl":"https://doi.org/10.22456/2175-2745.94082","url":null,"abstract":"The Prize Collecting Traveling Salesman Problem with Ridesharing is a model that joins elements from the Prize Collecting Traveling Salesman and the collaborative transport. The salesman is the driver of a capacitated vehicle and uses a ridesharing system to minimize travel costs. There are a penalty and a bonus associated with each vertex of a graph, G, that represents the problem. There is also a cost associated with each edge of G. The salesman must choose a subset of vertices to be visited so that the total bonus collection is at least a given a parameter. The length of the tour plus the sum of penalties of all vertices not visited is as small as possible. There is a set of persons demanding rides. The ride request consists of a pickup and a drop off location, a maximum travel duration, and the maximum amount the person agrees to pay. The driver shares the cost associated with each arc in the tour with the passengers in the vehicle. Constraints from ride requests, as well as the capacity of the car, must be satisfied. We present a mathematical formulation for the problem investigated in this study and solve it in an optimization tool. We also present three heuristics that hybridize exact and heuristic methods. These algorithms use a decomposition strategy that other enriched vehicle routing problems can utilize.","PeriodicalId":82472,"journal":{"name":"Research initiative, treatment action : RITA","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91265601","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}

引用次数: 3

Studying the Performance of Cognitive Models in Time Series Forecasting 认知模型在时间序列预测中的性能研究

Research initiative, treatment action : RITA Pub Date : 2020-01-15 DOI: 10.22456/2175-2745.96181

A. B. S. Neto, T. Ferreira, M. D. C. M. Batista, P. Firmino

引用次数: 0

A Computational Strategy for Classification of Enem Issues Based on Item Response Theory 一种基于项目反应理论的问题分类计算策略

Research initiative, treatment action : RITA Pub Date : 2020-01-15 DOI: 10.22456/2175-2745.92406

G. H. Nunes, B. A. Oliveira, C. Nametala

{"title":"A Computational Strategy for Classification of Enem Issues Based on Item Response Theory","authors":"G. H. Nunes, B. A. Oliveira, C. Nametala","doi":"10.22456/2175-2745.92406","DOIUrl":"https://doi.org/10.22456/2175-2745.92406","url":null,"abstract":"The National High School Examination (ENEM) gains each year more importance, as it gradually, replacing traditional vestibular. Many simulations are done almost randomly by teachers or systems, with questions chosen without discretion. With this methodology, if a test needs to be reapplied, it is not possible to recreate it with questions that have the same difficulty as those used in the first evaluation. In this context, the present work presents the development of an ENEM Intelligent Simulation Generation System that calculates the parameters of Item Response Theory (TRI) of questions that have already been applied in ENEM and, based on them, classifies them. in groups of difficulty, thus enabling the generation of balanced tests. For this, the K-means algorithm was used to group the questions into three difficulty groups: easy, medium and difficult. To verify the functioning of the system, a simulation with 180 questions was generated along the ENEM model. It could be seen that in 37.7% of cases this happened. This hit rate was not greater because the algorithm confounded the difficulty of issues that are in close classes. However, the system has a hit rate of 92.8% in the classification of questions that are in distant groups.","PeriodicalId":82472,"journal":{"name":"Research initiative, treatment action : RITA","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81066039","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}

引用次数: 0

Evaluating the impact of maintenance policies associated to SLA contracts on the dependability of data centers electrical infrastructures 评估与SLA合同相关的维护策略对数据中心电力基础设施可靠性的影响

Research initiative, treatment action : RITA Pub Date : 2020-01-15 DOI: 10.22456/2175-2745.88822

Felipe Fernandes Lima Melo, J. F. D. S. Junior, G. Callou

引用次数: 0

Group Labeling Methodology Using Distance-based Data Grouping Algorithms 使用基于距离的数据分组算法的分组标记方法

Research initiative, treatment action : RITA Pub Date : 2020-01-15 DOI: 10.22456/2175-2745.91414

Francisco das Chagas Imperes Filho, V. Machado, R. Veras, K. Aires, Aline Montenegro Leal Silva

{"title":"Group Labeling Methodology Using Distance-based Data Grouping Algorithms","authors":"Francisco das Chagas Imperes Filho, V. Machado, R. Veras, K. Aires, Aline Montenegro Leal Silva","doi":"10.22456/2175-2745.91414","DOIUrl":"https://doi.org/10.22456/2175-2745.91414","url":null,"abstract":"Clustering algorithms are often used to form groups based on the similarity of their members. In this context, understanding a group is just as important as its composition. Identifying, or labeling groups can assist with their interpretation and, consequently, guide decision-making efforts by taking into account the features from each group. Interpreting groups can be beneficial when it is necessary to know what makes an element a part of a given group, what are the main features of a group, and what are the differences and similarities among them. This work describes a method for finding relevant features and generate labels for the elements of each group, uniquely identifying them. This way, our approach solves the problem of finding relevant definitions that can identify groups. The proposed method transforms the standard output of an unsupervised distance-based clustering algorithm into a Pertinence Degree (GP), where each element of the database receives a GP concerning each formed group. The elements with their GPs are used to formulate ranges of values for their attributes. Such ranges can identify the groups uniquely. The labels produced by this approach averaged 94.83% of correct answers for the analyzed databases, allowing a natural interpretation of the generated definitions.","PeriodicalId":82472,"journal":{"name":"Research initiative, treatment action : RITA","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77802522","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}

引用次数: 3

PHOC Descriptor Applied for Mammography Classification PHOC描述符用于乳腺摄影分类

Research initiative, treatment action : RITA Pub Date : 2020-01-15 DOI: 10.22456/2175-2745.89115

G. B. Santos, André Tragancin Filho

引用次数: 3

An Online Tree-Based Approach for Mining Non-Stationary High-Speed Data Streams 一种基于在线树的非平稳高速数据流挖掘方法

Research initiative, treatment action : RITA Pub Date : 2020-01-15 DOI: 10.22456/2175-2745.90822

Agustín Alejandro Ortiz Díaz, Isvani Inocencio Frías Blanco, L. M. Mariño, F. Baldo

{"title":"An Online Tree-Based Approach for Mining Non-Stationary High-Speed Data Streams","authors":"Agustín Alejandro Ortiz Díaz, Isvani Inocencio Frías Blanco, L. M. Mariño, F. Baldo","doi":"10.22456/2175-2745.90822","DOIUrl":"https://doi.org/10.22456/2175-2745.90822","url":null,"abstract":"This paper presents a new learning algorithm for inducing decision trees from data streams. In these domains, large amounts of data are constantly arriving over time, possibly at high speed. The proposed algorithm uses a top-down induction method for building trees, splitting leaf nodes recursively, until none of them can be expanded. The new algorithm combines two split methods in the tree induction. The first method is able to guarantee, with statistical significance, that each split chosen would be the same as that chosen using infinite examples. By doing so, it aims at ensuring that the tree induced online is close to the optimal model. However, this split method often needs too many examples to make a decision about the best split, which delays the accuracy improvement of the online predictive learning model. Therefore, the second method is used to split nodes more quickly, speeding up the tree growth. The second split method is based on the observation that larger trees are able to store more information about the training examples and to represent more complex concepts. The first split method is also used to correct splits previously suggested by the second one, when it has sufficient evidence. Finally, an additional procedure rebuilds the tree model according to the suggestions made with an adequate level of statistical significance. The proposed algorithm is empirically compared with several well-known induction algorithms for learning decision trees from data streams. In the tests it is possible to observe that the proposed algorithm is more competitive in terms of accuracy and model size using various synthetic and real world datasets.","PeriodicalId":82472,"journal":{"name":"Research initiative, treatment action : RITA","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75882151","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}

引用次数: 0