2010 IEEE International Conference on Data Mining Workshops最新文献_第5页

Trading Tests of Long-Term Market Forecast by Text Mining 基于文本挖掘的长期市场预测交易检验

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.60

K. Izumi, Takashi Goto, Tohgoroh Matsui

引用次数: 8

PTCR-Miner: Progressive Temporal Class Rule Mining for Multivariate Temporal Data Classification PTCR-Miner:用于多变量时态数据分类的渐进时态类规则挖掘

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.171

Chao-Hui Lee, V. Tseng

{"title":"PTCR-Miner: Progressive Temporal Class Rule Mining for Multivariate Temporal Data Classification","authors":"Chao-Hui Lee, V. Tseng","doi":"10.1109/ICDMW.2010.171","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.171","url":null,"abstract":"Recently, multivariate temporal data classification has been widely applied on many fields, such as bio-signals analysis, stocks prediction and weather forecasting. Multivariate temporal data contains hybrid type of attributes like numeric and categorical ones. However, most classification methods proposed in the past researches are not directly applicable to the multivariate temporal data with multiple types. Additionally, no useful and readable rules are provided in the existing methods for advanced classification analysis. In this paper, we proposed a novel algorithm named Progressive Temporal Class Rule Miner (PTCR-Miner) for classification on multivariate temporal data with a rule-based design. Through our algorithm, the classification rules discovered follow the purification concept we defined to be comprehensible and intuitive for general users to use on data classification. A series of experiments were conducted to evaluate our method with a multivariate temporal data simulator. The experimental results showed that PTCR-Miner performs effectively and efficiently on different simulated multivariate temporal datasets. Additionally, a real dataset related to asthma monitoring was also tested and the results showed that our classification mechanism works stably for asthma attack predictions. This means the discovered rules are really helpful and comprehensible for data classification. Furthermore, the rule-based and flexible architecture make PTCR-Miner more applicable to different application areas of multivariate temporal data classification.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"76 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114147515","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

Unsupervised DRG Upcoding Detection in Healthcare Databases 医疗保健数据库中的无监督DRG升级检测

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.108

Wei Luo, M. Gallagher

引用次数: 20

An Empirical Comparison of Platt Calibration and Inductive Confidence Machines for Predictions in Drug Discovery 药物发现预测中普拉特校准和归纳置信机的实证比较

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.111

Nikil Wale

{"title":"An Empirical Comparison of Platt Calibration and Inductive Confidence Machines for Predictions in Drug Discovery","authors":"Nikil Wale","doi":"10.1109/ICDMW.2010.111","DOIUrl":"https://doi.org/10.1109/ICDMW.2010.111","url":null,"abstract":"During the early phase of drug discovery, machine learning methods are often utilized to select compounds to send for experimental screening. In order to accomplish this goal, any method that can provide estimates of error rate for a given set of predictions is an extremely valuable tool. In this paper we compare Platt Calibration Algorithm and recently introduced Conformal Algorithm to control the error rate in the sense of precision while preserving the ability to identify as many compounds as possible (recall) that are highly likely to be bio-active in a certain context. We empirically evaluate and compare the performance of Platt’s Calibration and offline Mondrian ICM in the context of SVM-based classification on 75 distinct classification problems. We perform this evaluation in the real world setting where the true class labels of compounds are unknown at the time of prediction and are only revealed after the biological experiment is completed. Our empirical results show that under this setting, offline Mondrian ICM and Platt Calibration are not able to bound precision rates very well on an absolute basis. Comparatively, Mondrian ICM, even though not theoretically designed to control precision directly, compares favorably with Platt Calibration for this task.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116400542","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

Efficient Dimensionality Reduction on Undersampled Problems through Incremental Discriminative Common Vectors 基于增量判别公共向量的欠采样问题有效降维

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.50

F. Ferri, Katerine Díaz-Chito, W. D. Villanueva

引用次数: 4

Mining Users' Opinions Based on Item Folksonomy and Taxonomy for Personalized Recommender Systems 基于条目分类法的个性化推荐系统用户意见挖掘

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.163

Huizhi Liang, Yue Xu, Yuefeng Li

引用次数: 8

A Study on the Accuracy of Frequency Measures and Its Impact on Knowledge Discovery in Single Sequences 单序列中频率度量的准确性及其对知识发现的影响研究

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.83

M. Gan, H. Dai

引用次数: 9

Learning Robust Bayesian Network Classifiers in the Space of Markov Equivalent Classes 在马尔可夫等价类空间中学习稳健贝叶斯网络分类器

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.91

Zhongfeng Wang, Zhihai Wang, Bin Fu

引用次数: 0

An Integrative Scoring Approach to Identify Transcriptional Regulations Controlling Lung Surfactant Homeostasis 一种综合评分方法识别控制肺表面活性物质稳态的转录调控

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.110

Minlu Zhang, C. Fang, Yan Xu, R. Bhatnagar, L. Lu

引用次数: 1

Computing Popular Places Using Graphics Processors 使用图形处理器计算热门场所

2010 IEEE International Conference on Data Mining Workshops Pub Date : 2010-12-13 DOI: 10.1109/ICDMW.2010.45

Marta Fort, J. A. Sellarès, Nacho Valladares

引用次数: 4