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Modelling In-store Consumer Behaviour Using machine learning and Digital Signage Audience Measurement Data 1

1st international workshop on Video Analytics for Audience Measurement (VAAM)

作者： Ravnik, Robert Solina, Franc Zabkar, Vesna Univ Ljubljana Fac Comp & Informat Sci Ljubljana Slovenia Univ Ljubljana Fac Econ Ljubljana Slovenia

ISBN: (纸本)9783319128115;9783319128108

Audience adaptive digital signage is a new emerging technology, where public broadcasting displays adapt their content to the audience demographic and temporal features. The collected audience measurement data can be used as a unique basis for statistical analysis of viewing patterns, interactive display applications and also for further research and observer modelling. Here, we use machine learning methods on real-world digital signage viewership data to predict consumer behaviour in a retail environment, especially oriented towards the purchase decision process and the roles in purchasing situations. A case study is performed on data from a small retail shop where demographic and audience data of 1294 store customers were collected, manually verified and analysed. Among all customers, 246 store customers were involved in a buying process that resulted in an actual purchase. Comparison of different machine learning methods shows that by using support vector machines we can predict with 88.6% classification accuracy whether a customer will actually make a purchase, which outperforms classification accuracy of a baseline (majority) classifier by 7.5 %. A similar approach can also be used to predict the roles of an individual in the purchase decision process. We show that by extending the audience measurement dataset with additional heuristic features, the support vector machines classifier on average improves the classification accuracy of a baseline classifier by 15%.

关键词： Support vector machines

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Online Audience Measurement System Based on machine learning Techniques

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1st international workshop on Video Analytics for Audience Measurement (VAAM)

作者： Khryashchev, Vladimir Priorov, Andrey Ganin, Alexander PG Demidov Yaroslavl State Univ Yaroslavl Russia

ISBN: (纸本)9783319128115;9783319128108

An application for video data analysis based on computer vision methods is presented. The proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of tracking. Novel gender and age classifiers based on adaptive features, local binary patterns and support vector machines are proposed. More than 92 % accuracy of viewer's gender recognition is achieved. All the stages are united into a single system of audience analysis. The system allows to extract all the possible information about depicted people from the input video stream, to aggregate and analyze this information in order to measure different statistical parameters.

关键词： Online audience measurement machine learning Gender classification Age estimation Local binary patterns Support vector machines

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A hybrid approach to finding negated and uncertain expressions in biomedical documents

A hybrid approach to finding negated and uncertain expressio...

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2nd international workshop on Managing Interoperability and Complexity in Health Systems, MIX-HS 2012, Collocated with the 21st ACM international Conference on Information and Knowledge Management, CIKM 2012

作者： Fujikawa, Kazuki Seki, Kazuhiro Uehara, Kuniaki Kobe University 1-1 Rokkodai Nada Kobe Japan

ISBN: (纸本)9781450317214

More and more biomedical documents are digitally written and stored. To make the most of the rich resources, it is crucial to precisely locate the information pertinent to users' interests. One of the obstacles in finding information in natural language text is negations, which deny or reverse the meaning of a sentence. This is especially problematic in the biomedical domain since scientific findings and clinical records often contain negated expressions to state negative effects or the absence of symptoms. Ignoring such negated expressions result in more irrelevant information and may even lead to false conclusions. Therefore, identifying negative words and their scopes are important sub-tasks in biomedical information processing. This paper reports on our ongoing work on a hybrid approach to negation identification combining statistical and heuristic approaches. Our approach is evaluated on three types of biomedical documents in comparison with an existing machine learning approach. In addition, the empirical results are manually analyzed to better understand the nature of the problems. Copyright © 2012 ACM.

关键词： Heuristic methods

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Content-Based Medical Image Retrieval with Metric learning via Rank Correlation

Content-Based Medical Image Retrieval with Metric Learning v...

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1st international workshop on machine learning in Medical Imaging

作者： Huang, Wei Chan, Kap Luk Li, Huiqi Lim, Joo Hwee Liu, Jiang Wong, Tien Yin Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Inst Infocomm Res Agcy Sci Technol & Res Technol & Res Singapore Singapore Natl Univ Singapore Singapore Natl Eye Ctr Eye Res Inst Singapore 117548 Singapore

ISBN: (纸本)9783642159473

A novel content-based medical image retrieval method with metric learning via rank correlation is proposed in this paper. A new rank correlation measure is proposed to learn a metric encoding the pairwise similarity between images via direct optimization. Our method has been evaluated with a large population-based dataset composed of 5000 slit-lamp images with different nuclear cataract severities. Experimental results and statistical analysis demonstrate the superiority of our method over several popular metric learning methods in content-based slit-lamp image retrieval.

关键词： Medical imaging

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Improving accuracy of identifying clinical concepts in noisy unstructured clinical notes using existing internal redundancy

Improving accuracy of identifying clinical concepts in noisy...

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4th workshop on Analytics for Noisy Unstructured Text Data, AND'10 Co-located with 19th international Conference on Information and Knowledge Management, CIKM'10

作者： Patrick, Jon David Asgari, Pooyan Motamedi, Negin Health Information Technologies Research Laboratory School of IT University of Sydney 1 Cleveland St NSW 2006 Australia Nepean Hospital PO Box 63 Penrith NSW 2750 Australia

ISBN: (纸本)9781450303767

This paper, reports on the results of research which is based originally on the 2009 criteria and corpus of "The Obesity Challenge", defined by Informatics for Integrating Biology and the Bedside (i2b2), a National Center for Biomedical Computing. In the original task, i2b2 asked participants to build software systems that could process a corpus of noisy patient's clinical discharge summaries and report on patients' condition. The ultimate aim was to compare the judgments of physicians in evaluating the patient condition to a machine performance over such a corpus. The authors used a collection of resources to lexically and semantically characterize the diseases and their associated signs, symptoms. In this approach, they combined dictionary look-up, rule-based, and machine-learning methods along with adopting a special internal redundancy algorithm to reduce the usage of customized rules and increase the consistency of the performance over various types of noisy corpora. The performance was strengthened by information extracted from the patient notes via an internal redundancy module to overcome False Positives (FPs) and False Negatives (FNs) arising from the noisy nature of corpus. The methods were applied to a collection of 507 previously unseen noisy patient discharge summaries, and the Judgments were evaluated against a manually provided gold standard. The overall ranking of the participating Research groups were primarily based on the macro-averaged F-measure over 16 Classes of diseases. The implemented method achieved the micro-averaged F-measure of 96.9% (ranked within the top 7 out of 28 research groups) where there was no statistical significant difference between top 7 teams in micro F-measure. The highest F-Measure was 97.2%. The performance achieved was in line with the agreement between human annotators, indicating the potential of text mining for accurate and efficient prediction of disease status from clinical discharge summaries. Comparison of the r

关键词： Redundancy

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Research and Improvement on Bintree Multi-class Categorization Algorithm Based on SVM

Research and Improvement on Bintree Multi-class Categorizati...

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1st international workshop on Database Technology and Applications

作者： Hu Yan Li Min Xiong Hao-yong Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China

ISBN: (纸本)9780769536040

It's a hotspot to expend the research on support vector machine from a two-class issue to a multi-class one. Among all kinds of methods, Bintree multi-class text categorization algorithm based on support vector machine is more effective in training and sorting then others, and it works out the impartibility problem. So it is a good method. The dissertation systematically researches and analyses Bintree multi-class text categorization algorithm based on support vector machine, and improves it. That is, assemble first, and then sort them when the size of testing texts is too large. The aim is that after improvement the judgment of the testing text does not have to begin from the base crunode of Bintree, instead the testing text can be put into category function to be computed The improvement can enhance the efficiency of text categorization and the probability of accurate categorization when the size of testing texts is too big and the quantity of sorted functions is too large.

关键词： Support Vector machine Categorization Algorithm statistical learning Theory Quadratic Programming

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machine learning in Genome-Wide Association studies

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GENETIC EPIDEMIOLOGY 2009年第Sup1期33卷 S51-S57页

作者： Szymczak, Silke Biernacka, Joanna M. Cordell, Heather J. Gonzalez-Recio, Oscar Koenig, Inke R. Zhang, Heping Sun, Yan V. Med Univ Lubeck Inst Med Biometrie & Stat D-23562 Lubeck Germany Mayo Clin Dept Hlth Sci Res Rochester MN USA Univ Newcastle Inst Human Genet Int Ctr Life Newcastle Upon Tyne Tyne & Wear England Yale Univ Dept Epidemiol & Publ Hlth Sch Med New Haven CT 06520 USA Univ Wisconsin Dept Dairy Sci Madison WI 53706 USA Univ Michigan Sch Publ Hlth Dept Epidemiol Ann Arbor MI 48109 USA

Recently, genome-wide association studies have substantially expanded our knowledge about genetic variants that influence the susceptibility to complex diseases. Although standard statistical tests for each single-nucleotide polymorphism (SNP) separately are able to capture main genetic effects, different approaches are necessary to identify SNPs that influence disease risk jointly or in complex interactions. Experimental and simulated genome-wide SNP data provided by the Genetic Analysis workshop 16 afforded an opportunity to analyze the applicability and benefit of several machine learning methods. Penalized regression, ensemble methods, and network analyses resulted in several new findings while known and simulated genetic risk variants were also identified. In conclusion, machine learning approaches are promising complements to standard single-and multi-SNP analysis methods for understanding the overall genetic architecture of complex human diseases. However, because they are not optimized for genome-wide SNP data, improved implementations and new variable selection procedures are required. Genet. Epidemiol. 33 (Suppl. 1):S51-S57, 2009. (C) 2009 Wiley-Liss, Inc.

关键词： Genetic Analysis workshop data mining penalized regression random forests network analysis

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The Challenge of Detecting Epistasis (G x G Interactions): Genetic Analysis workshop 16

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GENETIC EPIDEMIOLOGY 2009年第Sup1期33卷 S58-S67页

作者： An, Ping Mukherjee, Odity Chanda, Pritam Yao, Li Engelman, Corinne D. Huang, Chien-Hsun Zheng, Tian Kovac, Ilija P. Dube, Marie-Pierre Liang, Xueying Li, Jia de Andrade, Mariza Culverhouse, Robert Malzahn, Doerthe Manning, Alisa K. Clarke, Geraldine M. Jung, Jeesun Province, Michael A. Washington Univ Sch Med Div Stat Genom St Louis MO 63108 USA Washington Univ Sch Med Dept Genet St Louis MO 63108 USA Natl Ctr Biol Sci Bangalore Karnataka India SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA Univ Wisconsin Dept Human Dev & Family Studies Madison WI USA Columbia Univ Dept Stat New York NY USA Montreal Heart Inst Res Ctr Montreal PQ H1T 1C8 Canada NCI Div Canc Epidemiol & Genet NIH DHHS Bethesda MD 20892 USA Mayo Clin Dept Hlth Sci Res Rochester MN USA Washington Univ Sch Med Dept Med St Louis MO 63110 USA Univ Gottingen Dept Genet Epidemiol Univ Med Ctr Gottingen Germany Boston Univ Sch Publ Hlth Boston MA USA Univ Oxford Wellcome Trust Ctr Human Genet Oxford England Indiana Univ Sch Med Dept Med & Mol Genet Indianapolis IN 46202 USA

Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework. Genet. Epidetniol. 33 (Suppl. 1):S58-S67, 2009. (C) 2009 Wiley-Liss, Inc.

关键词： generalized linear model machine learning methods

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Functional semantic categories for art history text - Human labeling and preliminary machine learning

Functional semantic categories for art history text - Human ...

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1st international workshop on Metadata Mining for Image Understanding, MMIU 2008 - In Conjunction with VISIGRAPP 2008

作者： Passonneau, Rebecca J. Yano, Tae Lippincott, Tom Klavans, Judith Center for Computational Learning Systems Columbia University United States Department of Computer Science Carnegie Mellon University United States Department of Computer Science Columbia University United States College of Information Studies University of Maryland United States

ISBN: (纸本)9789898111241

The CLiMB project investigates semi-automatic methods to extract descriptive metadata from texts for indexing digital image collections. We developed a set of functional semantic categories to classify text extracts that describe images. Each semantic category names a functional relation between an image depicting a work of art historical significance, and expository text associated with the image. This includes description of the image, discussion of the historical context in which the work was created, and so on. We present interannotator agreement results on human classification of text extracts, and accuracy results from initial machine learning experiments. In our pilot studies, human agreement varied widely, depending the labeler's expertise, the image-text pair under consideration, the number of labels that could be assigned to one text, and the type of training, if any, we gave labelers. Initial machine learning results indicate the three most relevant categories are machine learnable. Based on our pilot work, we implemented a labeling interface that we are currently using to collect a large dataset of text that will be used in training and testing machine classifiers.

关键词： statistical tests

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study of the learning model based on improved ID3 algorithm

Study of the learning model based on improved ID3 algorithm

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1st international workshop on Knowledge Discovery and Data Mining

作者： Ding Rongtao Ji Xinhao Zhu Linting Ren Wei Zhejiang Vocat Coll Commerce Hangzhou Peoples R China

ISBN: (纸本)9780769530901

The network learning behavior intelligence analysis system can collect the information of learner's psychology, behavior, methods and effectiveness in the learning process, and classify;learners by using the ID3 algorithm based on the internal factors and personality characteristics of learners that influence the learning effect. In order to correct the shortcomings that the ID3 algorithm more inclined to the attributes that have more values in the classification process, we introduce user interest, which used to distinguish the dependence between different information attributes. At the same time, we introduce parameters to reduce the redundancy between attributes, and accelerate the pace of information entropy reducing, then construct a general, expandable senior vocational student model in the intelligence-learning environment.

关键词： Decision trees Mutual information Data mining Educational institutions Business Classification tree analysis statistical analysis Intelligent systems Intelligent networks Algorithm design and analysis

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