2013 12th International Conference on Machine Learning and Applications最新文献_第4页

Recommending Messages to Users in Social Networks: A Cross-Site Study 在社交网络中向用户推荐信息:一项跨站点研究

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.160

R. Cohen, N. Sardana, K. Rahim, D. Lam, M. Li, O. Maccarthy, E. Woo, Jie Zhang, G. Guo

引用次数: 3

Simplifying the Utilization of Machine Learning Techniques for Bioinformatics 简化机器学习技术在生物信息学中的应用

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.155

D. Dittman, T. Khoshgoftaar, Randall Wald, Amri Napolitano

{"title":"Simplifying the Utilization of Machine Learning Techniques for Bioinformatics","authors":"D. Dittman, T. Khoshgoftaar, Randall Wald, Amri Napolitano","doi":"10.1109/ICMLA.2013.155","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.155","url":null,"abstract":"The domain of bioinformatics has a number of challenges such as handling datasets which exhibit extreme levels of high dimensionality (large number of features per sample) and datasets which are particularly difficult to work with. These datasets contain many pieces of data (features) which are irrelevant and redundant to the problem being studied, which makes analysis quite difficult. However, techniques from the domain of machine learning and data mining are well suited to combating these difficulties. Techniques like feature selection (choosing an optimal subset of features for subsequent analysis by removing irrelevant or redundant features) and classifiers (used to build inductive models in order to classify unknown instances) can assist researchers in working with such difficult datasets. Unfortunately, many practitioners of bioinformatics do not have the machine learning knowledge to choose the correct techniques in order to achieve good classification results. If the choices could be simplified or predetermined then it would be easier to apply the techniques. This study is a comprehensive analysis of machine learning techniques on twenty-five bioinformatics datasets using six classifiers, and twenty-four feature rankers. We analyzed the factors at each of four feature subset sizes chosen for being large enough to be effective in creating inductive models but small enough to be of use for further research. Our results shows that Random Forest with 100 trees is the top performing classifier and that the choice of feature ranker is of little importance as long as feature selection occurs. Statistical analysis confirms our results. By choosing these parameters, machine learning techniques are more accessible to bioinformatics.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115135028","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: 15

On-Line Incremental Learning for Unknown Conditions during Assembly Operations with Robots 机器人装配过程中未知条件的在线增量学习

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.101

Jose Luis Navarro Gonzalez, I. López-Juárez, K. Ordaz-Hernández

{"title":"On-Line Incremental Learning for Unknown Conditions during Assembly Operations with Robots","authors":"Jose Luis Navarro Gonzalez, I. López-Juárez, K. Ordaz-Hernández","doi":"10.1109/ICMLA.2013.101","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.101","url":null,"abstract":"To be effective in real operations where the environment is continuously changing, robots have to perceive the environment and to adapt accordingly. Unfortunately, there are uncertainties due to ageing of mechanisms, isturbances, backlash, etc. that limit the usage of current control algorithms. In this paper we propose an on-line incremental learning technique using Fuzzy ARTMAP and a pattern selection criterion. The technique starts by training the ANN with a primitive knowledge base. In the presence of new patterns, the criterion-based on the success of the current action-decides autonomously if the pattern should be learned, if the ANN has to recall, or if a recovery action must be performed. The incremental learning approach is based on the online update of the neural network weights and the defined criterion decides should the new pattern be learned. The peg in hole operation (PIH) is selected as the study case in order to evaluate the performance of the technique, which is described in detail as well as the basics of the peg in hole operation. Promising results obtained with real operations with an industrial robot without over training/forgetting is presented that validate the approach.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133972143","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: 1

Multi-modal Tree-Based SVM Classification 基于多模态树的SVM分类

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.19

Cecille Freeman, D. Kulić, O. Basir

引用次数: 3

Affect Detection and Classification from the Non-stationary Physiological Data 基于非平稳生理数据的影响检测与分类

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.49

Omar Alzoubi, Davide Fossati, S. D’Mello, R. Calvo

{"title":"Affect Detection and Classification from the Non-stationary Physiological Data","authors":"Omar Alzoubi, Davide Fossati, S. D’Mello, R. Calvo","doi":"10.1109/ICMLA.2013.49","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.49","url":null,"abstract":"Affect detection from physiological signals has received a great deal of attention recently. One arising challenge is that physiological measures are expected to exhibit considerable variations or non-stationarities over multiple days/sessions recordings. These variations pose challenges to effectively classify affective sates from future physiological data. The present study collects affective physiological data (electrocardiogram (ECG), electromyogram (EMG), skin conductivity (SC), and respiration (RSP)) from four participants over five sessions each. The study provides insights on how diagnostic physiological features of affect change over time. We compare the classification performance of two feature sets, pooled features (obtained from pooled day data) and day-specific features using an up datable classifier ensemble algorithm. The study also provides an analysis on the performance of individual physiological channels for affect detection. Our results show that using pooled feature set for affect detection is more accurate than using day-specific features. The corrugator and zygomatic facial EMGs were more reliable measures for detecting valence than arousal compared to ECG, RSP and SC over the span of multi-session recordings. It is also found that corrugator EMG features and a fusion of features from all physiological channels have the highest affect detection accuracy for both valence and arousal.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134413201","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

INDiC: Improved Non-intrusive Load Monitoring Using Load Division and Calibration 使用负载划分和校准改进的非侵入式负载监测

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.21

Nipun Batra, Haimonti Dutta, Amarjeet Singh

{"title":"INDiC: Improved Non-intrusive Load Monitoring Using Load Division and Calibration","authors":"Nipun Batra, Haimonti Dutta, Amarjeet Singh","doi":"10.1109/ICMLA.2013.21","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.21","url":null,"abstract":"Residential buildings contribute significantly to the overall energy consumption across most parts of the world. While smart monitoring and control of appliances can reduce the overall energy consumption, management and cost associated with such systems act as a big hindrance. Prior work has established that detailed feedback in the form of appliance level consumption to building occupants improves their awareness and paves the way for reduction in electricity consumption. Non-Intrusive Load Monitoring (NILM), i.e. the process of disaggregating the overall home electricity usage measured at the meter level into constituent appliances, provides a simple and cost effective methodology to provide such feedback to the occupants. In this paper we present Improved Non-Intrusive load monitoring using load Division and Calibration (INDiC) that simplifies NILM by dividing the appliances across multiple instrumented points (meters/phases) and calibrating the measured power. Proposed approach is used together with the Combinatorial Optimization framework and evaluated on the popular REDD dataset. Empirical results demonstrate significant improvement in disaggregation accuracy, achieved by using INDiC based Combinatorial Optimization, demonstrate significant improvement in disaggregation accuracy.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"113 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133391420","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: 34

Eigenfaces for Face Detection: A Novel Study 特征脸用于人脸检测:一个新的研究

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.63

Salaheddin Alakkari, J. Collins

引用次数: 6

Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression 基于生理模型和支持向量回归的血糖水平预测

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.30

Razvan C. Bunescu, Nigel Struble, C. Marling, J. Shubrook, F. Schwartz

{"title":"Blood Glucose Level Prediction Using Physiological Models and Support Vector Regression","authors":"Razvan C. Bunescu, Nigel Struble, C. Marling, J. Shubrook, F. Schwartz","doi":"10.1109/ICMLA.2013.30","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.30","url":null,"abstract":"Patients with diabetes must continually monitor their blood glucose levels and adjust insulin doses, striving to keep blood glucose levels as close to normal as possible. Blood glucose levels that deviate from the normal range can lead to serious short-term and long-term complications. An automatic prediction model that warned people of imminent changes in their blood glucose levels would enable them to take preventive action. Modeling inter-patient differences and the combined effects of insulin and life events on blood glucose have been particularly challenging in the design of accurate blood glucose forecasting systems. In this paper, we describe a solution that uses a generic physiological model of blood glucose dynamics to generate informative features for a Support Vector Regression model that is trained on patient specific data. Experimental results show that the new prediction model outperforms all three diabetes experts involved in the study, thus demonstrating the utility of using the generic physiological features in machine learning models that are individually trained for every patient.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114312133","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: 74

Determining Potential Yeast Longevity Genes via PPI Networks and Microarray Data Clustering Analysis 通过PPI网络和微阵列数据聚类分析确定潜在的酵母长寿基因

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.75

Bernard Chen, Roshan Doolabh, Fusheng Tang

{"title":"Determining Potential Yeast Longevity Genes via PPI Networks and Microarray Data Clustering Analysis","authors":"Bernard Chen, Roshan Doolabh, Fusheng Tang","doi":"10.1109/ICMLA.2013.75","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.75","url":null,"abstract":"Identification of genes involved in lifespan extension is a pre-requisite for studying aging and age-dependent diseases. So far, very few genes have been identified that relate to longevity. The process of analyzing each single gene one at a time can be a very long and expensive process. It is known that approximately 10% of 6000 yeast genes are lifespan related genes, however, less than 100 genes are identified as longevity genes. The interconnection of multiple genes and the time-dependent protein-protein interactions make researchers use systems biology as a first tool to predict genes potentially involved in aging. In this study, we combined analyses of protein-protein interaction data and micro array data to predict longevity genes. A dataset of all 6000 yeast genes was utilized and a protein-protein interaction ratio was used to narrow the dataset. Next, a hierarchical clustering algorithm was created to group the resulting data. From these clusters, conclusion of 6 highly possible longevity genes was drawn based on the amount of longevity genes in each cluster. Based on our latest information, one of our predicted genes is identified as a longevity gene. Wet lab experiments are applied to our predicted genes for supporting the findings.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115244877","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

Predicting Next Location of Twitter Users for Surveillance 预测Twitter用户的下一个监视地点

2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.134

S. Gunduz, U. Yavanoglu, Ş. Sağiroğlu

引用次数: 13