2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献_第3页

Mining toxicity structural alerts from SMILES: A new way to derive Structure Activity Relationships 从SMILES中挖掘毒性结构警报:一种导出结构活动关系的新方法

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949444

Thomas Ferrari, G. Gini, N. G. Bakhtyari, E. Benfenati

{"title":"Mining toxicity structural alerts from SMILES: A new way to derive Structure Activity Relationships","authors":"Thomas Ferrari, G. Gini, N. G. Bakhtyari, E. Benfenati","doi":"10.1109/CIDM.2011.5949444","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949444","url":null,"abstract":"Encouraged by recent legislations all over the world, aimed to protect human health and environment, in silico techniques have proved their ability to assess the toxicity of chemicals. However, they act often like a black-box, without giving a clear contribution to the scientific insight; such over-optimized methods may be beyond understanding, behaving more like competitors of human experts' knowledge, rather than assistants. In this work, a new Structure-Activity Relationship (SAR) approach is proposed to mine molecular fragments that act like structural alerts for biological activity. The entire process is designed to fit with human reasoning, not only to make its predictions more reliable, but also to enable a clear control by the user, in order to match customized requirements. Such an approach has been implemented and tested on the mutagenicity endpoint, showing marked prediction skills and, more interestingly, discovering much of the knowledge already collected in literature as well as new evidences. The achieved tool is a powerful instrument for both SAR knowledge discovery and for activity prediction on untested compounds.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129431837","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: 22

A robust F-measure for evaluating discovered process models 用于评估发现的过程模型的稳健f度量

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949428

Jochen De Weerdt, M. D. Backer, J. Vanthienen, B. Baesens

引用次数: 117

Active learning using the data distribution for interactive image classification and retrieval 主动学习利用数据分布进行交互式图像分类和检索

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949446

P. Blanchart, Marin Ferecatu, M. Datcu

{"title":"Active learning using the data distribution for interactive image classification and retrieval","authors":"P. Blanchart, Marin Ferecatu, M. Datcu","doi":"10.1109/CIDM.2011.5949446","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949446","url":null,"abstract":"In the context of image search and classification, we describe an active learning strategy that relies on the intrinsic data distribution modeled as a mixture of Gaussians to speed up the learning of the target class using an interactive relevance feedback process. The contributions of our work are twofold: First, we introduce a new form of a semi-supervised C-SVM algorithm that exploits the intrinsic data distribution by working directly on equiprobable envelopes of Gaussian mixture components. Second, we introduce an active learning strategy which allows to interactively adjust the equiprobable envelopes in a small number of feedback steps. The proposed method allows the exploitation of the information contained in the unlabeled data and does not suffer from the drawbacks inherent to semi-supervised methods, e.g. computation time and memory requirements. Tests performed on a database of high-resolution satellite images and on a database of color images show that our system compares favorably, in terms of learning speed and ability to manage large volumes of data, to the classic approach using SVM active learning.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123016308","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

Distinguishing defined concepts from prerequisite concepts in learning resources 在学习资源中区分已定义概念和先决概念

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949296

S. Changuel, Nicolas Labroche

引用次数: 3

A multi-Biclustering Combinatorial Based algorithm 一种基于多双聚类的组合算法

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949454

E. Nosova, G. Raiconi, R. Tagliaferri

引用次数: 1

Dimensionality reduction mappings 降维映射

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949443

K. Bunte, Michael Biehl, B. Hammer

引用次数: 12

Efficient accelerometer-based swimming exercise tracking 高效的基于加速度计的游泳运动跟踪

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949430

Pekka Siirtola, P. Laurinen, J. Röning, H. Kinnunen

{"title":"Efficient accelerometer-based swimming exercise tracking","authors":"Pekka Siirtola, P. Laurinen, J. Röning, H. Kinnunen","doi":"10.1109/CIDM.2011.5949430","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949430","url":null,"abstract":"The study concentrates on tracking swimming exercises based on the data of 3D accelerometer and shows that human activities can be tracked accurately using low sampling rates. The tracking of swimming exercise is done in three phases: first the swimming style and turns are recognized, secondly the number of strokes are counted and thirdly the intensity of swimming is estimated. Tracking is done using efficient methods because the methods presented in the study are designed for light applications which do not allow heavy computing. To keep tracking as light as possible it is studied what is the lowest sampling frequency that can be used and still obtain accurate results. Moreover, two different sensor placements (wrist and upper back) are compared. The results of the study show that tracking can be done with high accuracy using simple methods that are fast to calculate and with a really low sampling frequency. It is shown that an upper back-worn sensor is more accurate than a wrist-worn one when the swimming style is recognized, but when the number of strokes is counted and intensity estimated, the sensors give approximately equally accurate results.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126151134","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: 71

OLAP navigation in the Granular Linguistic Model of a Phenomenon 现象粒度语言模型中的OLAP导航

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949458

Carlos Menendez-Gonzalez, G. Triviño

{"title":"OLAP navigation in the Granular Linguistic Model of a Phenomenon","authors":"Carlos Menendez-Gonzalez, G. Triviño","doi":"10.1109/CIDM.2011.5949458","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949458","url":null,"abstract":"The amount of data provided by computers about objects in our environment increases. Nevertheless, the ability to extract relevant and understandable knowledge from this information remains limited. On-Line Analytical Processing (OLAP) is a well known paradigm used to help users to navigate by the information stored in databases. In the research line of Computational Theory of Perceptions, we have created the Granular Linguistic Model of a Phenomenon. It is a data structure that allows computational systems to generate linguistic descriptions of input data. In this paper, we explore the possibilities of using OLAP to navigate in this structure of information. Inspired in the way humans use NL, we adapt the typical operations in OLAP, namely, drilling, rolling and slicing/dicing, to navigate by hierarchical granular structures of fuzzy perceptions. The long term aim is to create a new type of human computer interface that will assist users in the analysis of the likely huge amount of available information about relevant phenomena. Obtained results show the viability of this approach including a practical demonstration of concept.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131603066","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: 6

Link Pattern Prediction with tensor decomposition in multi-relational networks 基于张量分解的多关系网络链接模式预测

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949306

Sheng Gao, Ludovic Denoyer, P. Gallinari

引用次数: 20

Trend cluster based compression of geographically distributed data streams 基于趋势聚类的地理分布数据流压缩

2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2011-04-11 DOI: 10.1109/CIDM.2011.5949298

A. Ciampi, A. Appice, D. Malerba, P. Guccione

引用次数: 7