2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献

Discovering process models through relational disjunctive patterns mining 通过关系析取模式挖掘发现流程模型

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949299

Corrado Loglisci, Michelangelo Ceci, A. Appice, D. Malerba

引用次数: 1

Active classifier training with the 3DS strategy 基于3DS策略的主动分类器训练

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949421

Tobias Reitmaier, B. Sick

{"title":"Active classifier training with the 3DS strategy","authors":"Tobias Reitmaier, B. Sick","doi":"10.1109/CIDM.2011.5949421","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949421","url":null,"abstract":"In this article, we introduce and investigate 3DS, a novel selection strategy for pool-based active training of a generative classifier, namely CMM (classifier based on a probabilistic mixture model). Such a generative classifier aims at modeling the processes underlying the “generation” of the data. The strategy 3DS considers the distance of samples to the decision boundary, the density in regions where samples are selected, and the diversity of samples in the query set that are chosen for labeling, e.g., by a human domain expert. The combination of the three measures in 3DS is adaptive in the sense that the weights of the distance and the density measure depend on the uniqueness of the classification. With nine benchmark data sets it is shown that 3DS outperforms a random selection strategy (baseline method), a pure closest sampling approach, ITDS (information theoretic diversity sampling), DWUS (density-weighted uncertainty sampling), DUAL (dual strategy for active learning), and PBAC (prototype based active learning) regarding evaluation criteria such as ranked performance based on classification accuracy, number of labeled samples (data utilization), and learning speed assessed by the area under the learning curve.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114373306","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: 11

Active learning for aspect model in recommender systems 面向方面模型的主动学习推荐系统

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949431

R. Karimi, C. Freudenthaler, A. Nanopoulos, L. Schmidt-Thieme

{"title":"Active learning for aspect model in recommender systems","authors":"R. Karimi, C. Freudenthaler, A. Nanopoulos, L. Schmidt-Thieme","doi":"10.1109/CIDM.2011.5949431","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949431","url":null,"abstract":"Recommender systems help Web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected specially for new users. Active learning has been proposed in the past, to acquire preference information from users. Based on an underlying prediction model, these approaches determine the most informative item for querying the new user to provide a rating. In this paper, we propose a new active learning method which is developed specially based on aspect model features. There is a difference between classic active learning and active learning for recommender system. In the recommender system context, each item has already been rated by training users while in classic active learning there is not training user. We take into account this difference and develop a new method which competes with a complicated bayesian approach in accuracy while results in drastically reduced (one order of magnitude) user waiting times, i.e., the time that the users wait before being asked a new query.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130867232","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: 23

An intelligent load forecasting expert system by integration of ant colony optimization, genetic algorithms and fuzzy logic 基于蚁群优化、遗传算法和模糊逻辑的智能负荷预测专家系统

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949432

A. Ghanbari, S. Abbasian-Naghneh, E. Hadavandi

{"title":"An intelligent load forecasting expert system by integration of ant colony optimization, genetic algorithms and fuzzy logic","authors":"A. Ghanbari, S. Abbasian-Naghneh, E. Hadavandi","doi":"10.1109/CIDM.2011.5949432","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949432","url":null,"abstract":"Computational intelligence (CI) as an offshoot of artificial intelligence (AI), is becoming more and more widespread nowadays for solving different engineering problems. Especially by embracing Swarm Intelligence techniques such as ant colony optimization (ACO), CI is known as a good alternative to classical AI for dealing with practical problems which are not easy to solve by traditional methods. Besides, electricity load forecasting is one of the most important concerns of power systems, consequently; developing intelligent methods in order to perform accurate forecasts is vital for such systems. This study presents a hybrid CI methodology (called ACO-GA) by integration of ant colony optimization, genetic algorithm (GA) and fuzzy logic to construct a load forecasting expert system. The superiority and applicability of ACO-GA is shown for Iran's annual electricity load forecasting problem and results are compared with adaptive neuro-fuzzy inference system (ANFIS), which is a common approach in this field. The outcomes indicate that ACO-GA provides more accurate results than ANFIS approach. Moreover, the results of this study provide decision makers with an appropriate simulation tool to make more accurate forecasts on future electricity loads.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126822695","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: 16

Flexible Heuristics Miner (FHM) 灵活启发式算法(FHM)

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949453

A. Weijters, J. Ribeiro

引用次数: 450

Feature extraction for multi-label learning in the domain of email classification 电子邮件分类领域中多标签学习的特征提取

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949301

José M. Carmona-Cejudo, Manuel Baena-García, J. D. Campo-Ávila, Rafael Morales Bueno

引用次数: 7

GSOM sequence: An unsupervised dynamic approach for knowledge discovery in temporal data GSOM序列:时间数据中知识发现的无监督动态方法

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949456

A. Fonseka, D. Alahakoon, S. Bedingfield

{"title":"GSOM sequence: An unsupervised dynamic approach for knowledge discovery in temporal data","authors":"A. Fonseka, D. Alahakoon, S. Bedingfield","doi":"10.1109/CIDM.2011.5949456","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949456","url":null,"abstract":"A significant problem which arises during the process of knowledge discovery is dealing with data which have temporal dependencies. The attributes associated with temporal data need to be processed differently from non temporal attributes. A typical approach to address this issue is to view temporal data as an ordered sequence of events. In this work, we propose a novel dynamic unsupervised learning approach to discover patterns in temporal data. The new technique is based on the Growing Self-Organization Map (GSOM), which is a structure adapting version of the Self-Organizing Map (SOM). The SOM is widely used in knowledge discovery applications due to its unsupervised learning nature, ease of use and visualization capabilities. The GSOM further enhances the SOM with faster processing, more representative cluster formation and the ability to control map spread. This paper describes a significant extension to the GSOM enabling it to be used to for analyzing data with temporal sequences. The similarity between two time dependent sequences with unequal length is estimated using the Dynamic Time Warping (DTW) algorithm incorporated into the GSOM. Experiments were carried out to evaluate the performance and the validity of the proposed approach using an audio-visual data set. The results demonstrate that the novel “GSOM Sequence” algorithm improves the accuracy and validity of the clusters obtained.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130122164","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: 5

Adaptive treatment of anemia on hemodialysis patients: A reinforcement learning approach 血液透析患者贫血的适应性治疗:强化学习方法

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949442

Pablo Escandell-Montero, J. Martínez-Martínez, J. Martín-Guerrero, E. Soria-Olivas, J. Vila-Francés, J. R. M. Benedito

引用次数: 2

A recommendation algorithm using positive and negative latent models 一种基于正潜和负潜模型的推荐算法

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949455

A. Takasu, Saranya Maneeroj

{"title":"A recommendation algorithm using positive and negative latent models","authors":"A. Takasu, Saranya Maneeroj","doi":"10.1109/CIDM.2011.5949455","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949455","url":null,"abstract":"This paper proposes an algorithm for recommender systems that uses both positive and negative latent user models. In recommending items to a user, recommender systems usually exploit item content information as well as the preferences of similar users. Various types of content information can be attached to items and these are useful for judging user preferences. For example, in movie recommendations, a movie record may include the director, the actors, and reviews. These types of information help systems calculate sophisticated user preferences. We first propose a probabilistic model that maps multi-attributed records into a low-dimensional feature space. The proposed model extends latent Dirichlet allocation to the handling of multi-attributed data. We derive an algorithm for estimating the model's parameters using the Gibbs sampling technique. Next, we propose a probabilistic model to calculate user preferences for items in the feature space. Finally, we develop a recommendation algorithm based on the probabilistic model that works efficiently for large quantities of items and user ratings. We use a publicly available movie corpus to evaluate the proposed algorithm empirically, in terms of both its recommendation accuracy and its processing efficiency.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124840477","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: 2

Visual tracking of the Millennium Development Goals with a Self-organizing neural network 基于自组织神经网络的千年发展目标视觉跟踪

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949433

Peter Sarlin

引用次数: 1