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Multi-label associative classification of medical documents from MEDLINE

Multi-label associative classification of medical documents ...

引用

4th international conference on machine learning and applications

作者： Rak, R Kurgan, L Reformat, M Univ Alberta Edmonton AB T6G 2V4 Canada

ISBN: (纸本)0769524958

Ability to provide convenient access to scientific documents becomes a difficult problem due to large and constantly increasing number of incoming documents and extensive manual work associated with their storage, description and classification. this requires intelligent search and classification capabilities for users to find required information. It is especially true for repositories of scientific medical articles due to their extensive use, large size and number of new documents, and well maintained structure. this research aims to provide an automated method for classification of articles into the structure of medical document repositories, which would support currently performed extensive manual work. the proposed method classifies articles from the largest medical repository, MEDLINE, using state of the art data mining technology. the method is based on a novel associative classification technique which considers recurrent items and most importantly multi-label characteristic of the MEDLINE data. Based on large scale experiments that utilize 350,000 documents several different classification algorithms have been compared including both recurrent and non-recurrent associative classification. the algorithms are capable of assigning each medical document to several classes (multi-label classification) and are characterized by relatively high accuracy. We also investigate different measures of classification quality and point out pros and cons of each. Based on experimental result we show that recurrent item based associative classification demonstrates superior performance and propose three alternative setups that allow the user to obtain different desired classification qualities.

关键词： Medical computing

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SEMI-SUPERVISED learning WIth BIDIRECTIONAL ADAPTIVE PAIRWISE ENCODING 15

SEMI-SUPERVISED LEARNING WITH BIDIRECTIONAL ADAPTIVE PAIRWIS...

引用

15th IEEE international conference on machine learning and applications (icmla)

作者： Yuan, Jiangbo Yu, Jie VipShop US Inc San Jose CA 95131 USA Canon USA Inc San Jose CA USA

ISBN: (纸本)9781509061679

In contrast to classic supervised learning methods that demand pre-defined class labels, pairwise encoding or side-information encoding merely requires pairwise similarity information to drive feature learning, which makes it very appealing for many fundamental tasks such as dimensionality reduction and semi-supervised learning. In this paper, we present a novel bimarginal pairwise encoding model, along with deep autoencoder, to learn nonlinear embedding for the aforementioned tasks. the new method learns powerful features that preserve critical pairwise information in a semi-supervised manner. It has achieved better performance on the well-known yet hard to make improvement benchmark MINIST compared with other methods in the same category, i.e. Autoencoder [4], Invariant Mapping for Dimensionality Reduction [1], Neighborhood Component Analysis [3], and Fixed Bi-Margin Pairwise Encoding [11].

关键词： Side-information deep auto-encoder metric learning dimensionality reduction semi-supervised learning

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Semantic Clone Detection Using machine learning 15

Semantic Clone Detection Using Machine Learning

引用

15th IEEE international conference on machine learning and applications (icmla)

作者： Sheneamer, Abdullah Kalita, Jugal Univ Colorado Colorado Springs CO 80918 USA

ISBN: (纸本)9781509061679

If two fragments of source code are identical to each other, they are called code clones. Code clones introduce difficulties in software maintenance and cause bug propagation. In this paper, we present a machine learning framework to automatically detect clones in software, which is able to detect Types-3 and the most complicated kind of clones, Type-4 clones. Previously used traditional features are often weak in detecting the semantic clones the novel aspects of our approach are the extraction of features from abstract syntax trees (AST) and program dependency graphs (PDG), representation of a pair of code fragments as a vector and the use of classification algorithms. the key benefit of this approach is that our approach can find both syntactic and semantic clones extremely well. Our evaluation indicates that using our new AST and PDG features is a viable methodology, since they improve detecting clones on the IJaDataset2.0.

关键词： Code clones Software clones Classifier algorithms Abstract syntax trees (AST) Program dependence graphs (PDG)

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Granular Computing with Compatibility Based Intuitionistic Fuzzy Rough Sets 16

Granular Computing with Compatibility Based Intuitionistic F...

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16th IEEE international conference on machine learning and applications (icmla)

作者： Bandyopadhyay, Sibasis Yao, JingTao Zhang, Yan Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada

ISBN: (纸本)9781538614174

In this paper, we are concerned with a problem related to granular computing with game theoretic intuitionistic fuzzy rough sets(GTIFRS). the game theoretic intuitionistic fuzzy rough sets is defined over compatibility based intuitionistic fuzzy relation. the imprecise information obtained, is considered to be composed of intuitionistic fuzzy granules. the basis of granulation is splitting intervals into several sub-intervals. A competitive situation arises due to movement of granules which is controlled by threshold parameters.

关键词： Rough sets Fuzzy sets Games Computational modeling Indexes Data mining Optimized production technology

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Adaptive thresholding and reweighting to improve domain transfer learning for unbalanced data With applications to EEG imbalance 15

Adaptive thresholding and reweighting to improve domain tran...

引用

15th IEEE international conference on machine learning and applications (icmla)

作者： Su, Kyung-min Robbins, Kay A. Hairston, W. David Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA US Army Res Lab Human Res & Engn Directorate Aberdeen Proving Ground MD 21005 USA

ISBN: (纸本)9781509061679

Domain adaptation methods can be highly sensitive to class balance, particularly the usually unknown balance of the unlabeled test set. In this work, we analyze the effect of imbalance on a well-known algorithm, ARTL (Adaptation Regularization Transfer learning) and propose four approaches for mitigating the adverse effects of imbalance. these include (1) balancing the training set for pseudo-label calculation, (2) applying adaptive thresholding to pseudo-label calculation, (3) using class reweighting in the optimization objective, and (4) applying adaptive thresholding to the output objective. We tested these methods with the UCI newsgroup dataset and on three types of imbalanced EEG (electroencephalogram) classification problems. We observed significant improvements, particularly for cases of extreme imbalance, which are not well addressed by standard classification techniques.

关键词： transfer learning imbalance adaptive threshold

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Forecasting Residential Energy Consumption: Single Household Perspective 17

Forecasting Residential Energy Consumption: Single Household...

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17th IEEE international conference on machine learning and applications (IEEE icmla)

作者： Zhang, Xiaoou Monica Grolinger, Katarina Capretz, Miriam A. M. Seewald, Luke Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada London Hydro London ON N6A 4H6 Canada

ISBN: (纸本)9781538668054

With the development of smart electricity metering technologies, huge amounts of consumption data can be retrieved on a daily and hourly basis. Energy consumption forecasting facilitates electricity demand management and utilities load planning. Most studies have been focussed on commercial customers or residential building-level energy consumption, or have used behavioral and occupancy sensor data to characterize an individual household's electrical consumption. this study has analyzed energy consumption at single household level using smart meter data to improve residential energy services and gain insights into planning demand response programs. Electricity consumption for anonymous individual households has been predicted using a Support Vector Regression (SVR) modelling with both daily and hourly data granularity. the electricity usage data set for 2014 to 2016 was obtained from a Canadian utility company. Exploratory data analysis (EDA) was used for data visualization and feature selection. the analysis presented here demonstrates that forecasting residential energy consumption for individual households is feasible, but the accuracy is highly dependable on household behaviour variability.

关键词： Forecasting Energy consumption Predictive models Meteorology Support vector machines Buildings Data models

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Identifying anomalies in parliamentary expenditures of Brazilian Chamber of Deputies with Deep Autoencoders 16

Identifying anomalies in parliamentary expenditures of Brazi...

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16th IEEE international conference on machine learning and applications (icmla)

作者： Gomes, thiago Alencar Carvalho, Rommel N. Carvalho, Ricardo S. Univ Brasilia UnB Dept Comp Sci CIC Brasilia DF Brazil

ISBN: (纸本)9781538614174

Each Brazilian Deputy receives a quota of money quota to cover the politician activity expenses, besides their salary. the amount of money reserved for that quota can sum up to almost 1 billion of Brazilian currency (approximately 300 million US Dollars) in a 4 year legislature. Civic society is using that data to perform independent auditing to verify expenses that are against the rules. this article presents the application of deep Autoencoders to identify anomalies in that data. the anomalies found indicate new suspicious expenses and several data quality problems in the data opened to the society.

关键词： Deep learning Autoencoder Anomaly Detection Open Data

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Vibration learning and Control Towards Vibration Actuated Robotic Systems 14

Vibration Learning and Control Towards Vibration Actuated Ro...

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IEEE 14th international conference on machine learning and applications icmla

作者： Joe, Woong Yeol Tennessee State Univ Dept Mech & Mfg Engn Nashville TN 37209 USA

ISBN: (纸本)9781509002870

this paper presents in-process research and details vibration analysis of a single actuator that can be used for robotic navigation and locomotion. the key steps to potential success of this works are ( 1) static and dynamic vibration analysis of generalized shapes for robots, ( 2) studies on solid mechanics based vibration propagation into the structure of the robot, ( 3) development of active control schemes of vibration isolation for the robot's navigation and locomotion purposes, and ( 4) analysis, simulation and design of passive leg mechanism for vibration driven robotic systems. Here, the author introduces state-of-the-art vibration driven robotic systems in the field, basic equations for a simple off-centered rotation mechanism, model and simulation/ test bed for vibration excitement, and introduction of active vibration control to one of control schemes for effective vibration isolation as preliminary work.

关键词： vibration active control isolation robot

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Training an Emergency-Response Image Classifier on Signal Data 17

Training an Emergency-Response Image Classifier on Signal Da...

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17th IEEE international conference on machine learning and applications (IEEE icmla)

作者： O'Neal, Aubrey Rodgers, Benjamin Segler, Justin Murthy, Dhiraj Lakuduva, Nandhini Johnson, Matthew Stephens, Keri K. Univ Texas Austin Dept Journalism Austin TX 78712 USA Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Univ Texas Austin Dept Commun Studies Austin TX 78712 USA

ISBN: (纸本)9781538668054

the increasing popularity of multimedia messages shared through public or private social media spills into diverse information dissemination contexts. To date, public social media has been explored as a potential alert system during natural disasters, but high levels of noise (i.e. non-relevant content) present challenges in both understanding social experiences of a disaster and in facilitating disaster recovery. this study builds on current research by uniquely using social media data, collected in the field through qualitative interviews, to create a supervised machine learning model. Collected data represents rescuers and rescuees during the 2017 Hurricane Harvey. Preliminary findings indicate a 99% accuracy in classifying data between signal and noise for signal-to-noise ratios (SNR) of 1:1, 1:2, 1:4, and 1:8. We also find 99% accuracy in classification between respondent types (volunteer rescuer, official rescuer, and rescuee). We furthermore compare human and machine coded attributes, finding that Google Vision API is a more reliable source of detecting attributes for the training set.

关键词： Emergency response crisis communication image classification machine learning

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Prediction of Users' Response Time in Q&A Communities 14

Prediction of Users' Response Time in Q&A Communities

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IEEE 14th international conference on machine learning and applications icmla

作者： Burlutskiy, Nikolay Fish, Andrew Ali, Nour Petridis, Miltos Univ Brighton Sch Comp Engn & Math Brighton BN2 4GJ E Sussex England

ISBN: (纸本)9781509002870

Social media and online Question and Answer (Q&A) communities in particular have become a successful solution for finding answers on diverse topics. However, not all questions are answered by these communities. Also, many questions are not answered quickly enough. In this paper, we propose a framework for predicting users' response time. the framework uses a diverse set of features including information on users, the content they generate while communicating, question tags, spatial and temporal features. then these features are used as input for training predictive models by various machine learning algorithms. As a case study, three diverse Q&A communities from Stack Exchange are selected to test the framework. We demonstrate that Deep Belief Networks outperform Logistic Regression (LR), k-nearest neighbors (k-NN), and Decision Trees (DT) in the accuracy of the prediction across the three diverse Q&A communities.

关键词： machine learning Social media Temporal user behavior

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