2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)最新文献

Prediction of SPEI Using MLR and ANN: A Case Study for Wilsons Promontory Station in Victoria 基于MLR和ANN的SPEI预测:以维多利亚Wilsons Promontory站为例

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-11 DOI: 10.1109/ICMLA.2015.87

Soukayna Mouatadid, R. Deo, J. Adamowski

引用次数: 7

Statistical Downscaling of Climate Change Scenarios of Rainfall and Temperature over Indira Sagar Canal Command Area in Madhya Pradesh, India 印度中央邦英迪拉萨加尔运河指挥区降雨和温度气候变化情景的统计降尺度

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-11 DOI: 10.1109/ICMLA.2015.75

Rituraj, Shukla

{"title":"Statistical Downscaling of Climate Change Scenarios of Rainfall and Temperature over Indira Sagar Canal Command Area in Madhya Pradesh, India","authors":"Rituraj, Shukla","doi":"10.1109/ICMLA.2015.75","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.75","url":null,"abstract":"General circulation models (GCMs) have been employed by climate agencies to predict future climate change. A challenging issue with GCM output for local relevance is their coarse spatial resolution of the projected variables. Statistical Downscaling Model (SDSM) identifies relationships between large-scale predictors (i.e., GCM-based) and local-scale predictands using multiple linear regression models. In this study (SDSM) was applied to downscale rainfall and temperature from GCMs. The data from single station located in the Indira Sagar canal command area at Madhya Pradesh, India were used as input of the SDSM. The study included calibration and validation with large-scale atmospheric variables encompassing the NCEP reanalysis data, the future estimation due to a climate scenario, which is HadCM3 A2. Results of the downscaling experiment demonstrate that during the calibration and validation stages, the SDSM model can be well acceptable regard its performance in the downscaling of daily rainfall and temperature. For a future period (2010-2099), the SDSM model estimated an increase in total average annual rainfall and annual average temperature for station. This indicates that the area of station considered will be wet and humid in the future. Also, the mean temperature is projected to rise to 1.5 C to 2.5 C for present study area. However, the model projections show a rise in mean daily precipitation with varying percentage in the months of July (0.59% to 2.09%) and August (0.79% to 1.19) under A2 of HadCM3 model for future periods.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115395123","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

Active Learning for One-Class Classification 一类分类的主动学习

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.167

Vincent Barnabe-Lortie, C. Bellinger, N. Japkowicz

{"title":"Active Learning for One-Class Classification","authors":"Vincent Barnabe-Lortie, C. Bellinger, N. Japkowicz","doi":"10.1109/ICMLA.2015.167","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.167","url":null,"abstract":"Active learning is a common solution for reducing labeling costs and maximizing the impact of human labeling efforts in binary and multi-class classification settings. However, when we are faced with extreme levels of class imbalance, a situation in which it is not safe to assume that we have a representative sample of the minority class, it has been shown effective to replace the binary classifiers with a one-class classifiers. In such a setting, traditional active learning methods, and many previously proposed in the literature for one-class classifiers, prove to be inappropriate, as they rely on assumptions about the data that no longer stand. In this paper, we propose a novel approach to active learning designed for one-class classification. The proposed method does not rely on many of the inappropriate assumptions of its predecessors and leads to more robust classification performance. The gist of this method consists of labeling, in priority, the instances considered to fit the learned class the least by previous iterations of a one-class classification model. We provide empirical evidence for the merits of the proposed method compared to the available alternatives, and discuss how the method may have an impact in an applied setting.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124872400","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: 23

Learning Context-Based Outcomes for Mobile Robots in Unstructured Indoor Environments 移动机器人在非结构化室内环境中基于上下文的学习结果

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.222

Priyam Parashar, Robert W. H. Fisher, R. Simmons, M. Veloso, Joydeep Biswas

{"title":"Learning Context-Based Outcomes for Mobile Robots in Unstructured Indoor Environments","authors":"Priyam Parashar, Robert W. H. Fisher, R. Simmons, M. Veloso, Joydeep Biswas","doi":"10.1109/ICMLA.2015.222","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.222","url":null,"abstract":"We present a method to learn context-dependent outcomes of behaviors in unstructured indoor environments. The idea is that certain features in the environment may be predictive of differences in outcomes, such as how long a mobile robot takes to traverse a corridor. Doing so enables the robot to plan more effectively, and also be able to interact with people more effectively by more accurately predicting when its plans may take longer to execute or may be likely to fail. We use a node-and-edge based map of the environment and treat the traversal time of the robot for each edge as a random variable to be characterized. The first step is to determine whether the distribution of the random variable is multimodal and, if so, we learn to classify the modes using a hierarchy of plan-time features (e.g., time of the day, day of the week) and run-time features (observations of recent traversal times through other corridors). We utilize a cascading regression system that first estimates which mode of the traversal distribution we expect the robot to observe, and then predict the actual traversal time through a corridor. On average, our method produces a mean residual error of less than 2.7 seconds.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124985116","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

Learning Common Metrics for Homogenous Tasks in Traffic Flow Prediction 交通流预测中同构任务的通用度量学习

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.188

Haikun Hong, Xiabing Zhou, Wenhao Huang, Xingxing Xing, Fei Chen, Yuntong Lei, Kaigui Bian, Kunqing Xie

{"title":"Learning Common Metrics for Homogenous Tasks in Traffic Flow Prediction","authors":"Haikun Hong, Xiabing Zhou, Wenhao Huang, Xingxing Xing, Fei Chen, Yuntong Lei, Kaigui Bian, Kunqing Xie","doi":"10.1109/ICMLA.2015.188","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.188","url":null,"abstract":"Nearest neighbor based nonparametric regression is a classic data-driven method for traffic flow prediction in intelligent transportation systems (ITS). Performances of those models depend heavily on the similarity or distance metric used to search nearest neighborhood. Metric learning algorithms have been developed to learn the distance metrics from data in recent years. In real-world transportation application, multiple forecasting tasks are set since there are lots of road sections and detector points in the traffic network. Previous works tend to learn only one global metric to be used for all the tasks or learn multiple local metrics for each task which may lead to under-fitting or over-fitting problem. To balance these two kinds of methods and improve the generalization of learned metrics, we propose a common metric learning algorithm under the intuition that homogenous tasks tend to have similar local metrics. Then the learned common metrics are used in common metric KNN (CM-KNN) for traffic flow prediction. Experimental results show that our algorithm to learn common metrics are reasonable and CM-KNN method for traffic flow prediction outperforms other competing methods.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126769163","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

Feature Selection Using Gustafson-Kessel Fuzzy Algorithm in High Dimension Data Clustering 基于Gustafson-Kessel模糊算法的高维数据聚类特征选择

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.57

G. Georgiev, N. Gueorguieva, Matthew Chiappa, Austin Krauza

{"title":"Feature Selection Using Gustafson-Kessel Fuzzy Algorithm in High Dimension Data Clustering","authors":"G. Georgiev, N. Gueorguieva, Matthew Chiappa, Austin Krauza","doi":"10.1109/ICMLA.2015.57","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.57","url":null,"abstract":"The performance of objective function-based fuzzy clustering algorithms depends on the shape and the volume of clusters, the initialization of clustering algorithm, the distribution of the data objects, and the number of clusters in the data. Feature selection is also one of the most important issues in high dimension data clustering specifically in bioinformatics, data mining, signal processing etc., where the feature space dimension tends to be very large, making both clustering and classification tasks very difficult. It is evident that the feature subset needed to successfully perform a given clustering and recognition task depends on the discriminatory qualities of the chosen features. We propose a new hybrid approach addressing feature selection, based on informative weights, which takes into account the membership degrees of the features performed by Gustafson-Kessel fuzzy algorithm. The purpose is to efficiently achieve high degree of dimensionality reduction and enhance or maintain predictive accuracy with selected features. The candidate feature subsets are generated by using iterative feature elimination procedure which results in estimation of feature informative weights. We use both supervised and unsupervised methods in order to evaluate the clustering abilities of feature subsets.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114459592","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

A Machine Learning Based WSN System for Autism Activity Recognition 基于机器学习的自闭症活动识别WSN系统

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.46

S. Al-Wakeel, B. Alhalabi, Hadi M. Aggoune, Mohammed M. Alwakeel

{"title":"A Machine Learning Based WSN System for Autism Activity Recognition","authors":"S. Al-Wakeel, B. Alhalabi, Hadi M. Aggoune, Mohammed M. Alwakeel","doi":"10.1109/ICMLA.2015.46","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.46","url":null,"abstract":"Autistic children often develop abnormal habits and in some cases they could be unsafe or even dangerous to themselves and their family members. Because of their limited speech ability, their inexperienced parents may underestimate their physical abilities compared to their intellectual level and may not realize that they could easily hurt themselves. Therefore, the need for an automatic alert system for autistic child parent assistance is great, and it will enhance the life experience for both the autistic child and the family. In this paper, we present a machine learning based electronic system for autism activity recognition using wireless sensor networks (WSNs). The system accurately detects autistic child gesture and motion. The system is named Autistic child Sensor and Assistant System (ACSA), and is comprised of three main components: the ACSA Wearable sensor device, the companion ACSA Parent Application and the machine learning algorithms developed for autistic movement event detection and processing. The paper describes the system concepts, its components and details of its architecture and operation. Individuals and families with Autism Spectrum Disorder children can utilize this system as alarming devices that assist them to protect their autistic child regardless of the environment. The proposed system is expected to enhance the life experience for all aides, the autistic child, the parents, and the autistic child whole family.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117304445","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: 24

Frequent Set Mining for Streaming Mixed and Large Data 流混合大数据的频繁集挖掘

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.218

R. Khade, Jessica Lin, Nital S. Patel

引用次数: 8

Deep Neural Networks with Parallel Autoencoders for Learning Pairwise Relations: Handwritten Digits Subtraction 用并行自编码器学习成对关系的深度神经网络:手写数字减法

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.175

Tianchuan Du, Li Liao

{"title":"Deep Neural Networks with Parallel Autoencoders for Learning Pairwise Relations: Handwritten Digits Subtraction","authors":"Tianchuan Du, Li Liao","doi":"10.1109/ICMLA.2015.175","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.175","url":null,"abstract":"Modelling relational data is a common task for many machine learning problems. In this work, we focus on learning pairwise relations between two entities, with deep neural networks. To incorporate the structural properties in the data that represent two entities concatenated together, two separate stacked autoencoders are introduced in parallel to extract individual features, which are then fed into a deep neural network for classification. The method is applied to a specific problem: whether two input handwritten digits differ by one. We tested the performance on a dataset generated from handwritten digits in MNIST, which is a widely used dataset for testing different machine learning techniques and pattern recognition methods. We compared with several different machine learning algorithms, including logistic regression and support vector machines, on this handwritten digit subtraction (HDS) dataset. The results showed that deep neural networks outperformed other methods, and in particular, the deep neural networks fitted with two separate autoencoders in parallel increased the prediction accuracy from 85.83%, which was achieved by a standard neural network with a single stacked autoencoder, to 88.27%.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127787464","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: 7

Random Forest with Random Projection to Impute Missing Gene Expression Data 基于随机投影的随机森林缺失基因表达数据的估算

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.29

Lovedeep Gondara

引用次数: 5