2016 International Joint Conference on Neural Networks (IJCNN)最新文献_第7页

Feature selection of autoregressive Neural Network inputs for trend Time Series Forecasting 趋势时间序列预测的自回归神经网络输入特征选择

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727378

S. Crone, Stephan Hager

引用次数: 9

Summarizing uncertain transaction databases by Probabilistic Tiles 用概率瓦片法总结不确定事务数据库

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727771

Chunyang Liu, Ling Chen

{"title":"Summarizing uncertain transaction databases by Probabilistic Tiles","authors":"Chunyang Liu, Ling Chen","doi":"10.1109/IJCNN.2016.7727771","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727771","url":null,"abstract":"Transaction data mining is ubiquitous in various domains and has been researched extensively. In recent years, observing that uncertainty is inherent in many real world applications, uncertain data mining has attracted much research attention. Among the research problems, summarization is important because it produces concise and informative results, which facilitates further analysis. However, there are few works exploring how to effectively summarize uncertain transaction data. In this paper, we formulate the problem of summarizing uncertain transaction data as Minimal Probabilistic Tile Cover Mining, which aims to find a high-quality probabilistic tile set covering an uncertain database with minimal cost. We define the concept of Probabilistic Price and Probabilistic Price Order to evaluate and compare the quality of tiles, and propose a framework to discover the minimal probabilistic tile cover. The bottleneck is to check whether a tile is better than another according to the Probabilistic Price Order, which involves the computation of a joint probability. We prove that it can be decomposed into independent terms and calculated efficiently. Several optimization techniques are devised to further improve the performance. Experimental results on real world datasets demonstrate the conciseness of the produced tiles and the effectiveness and efficiency of our approach.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123674119","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: 4

Enhancing temporal alignment with autoencoder regularization 用自编码器正则化增强时间对齐

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727840

Liquan Nie, Yuanyuan Wang, Xiang Zhang, Xuhui Huang, Zhigang Luo

引用次数: 3

Exploiting monotonicity constraints to reduce label noise: An experimental evaluation 利用单调性约束来降低标签噪声:一个实验评价

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727465

A. Feelders, Tijmen Kolkman

引用次数: 5

A metagenomic hybrid classifier for paediatric inflammatory bowel disease 儿童炎症性肠病的宏基因组杂交分类器

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727318

B. Wingfield, S. Coleman, T. McGinnity, A. Bjourson

{"title":"A metagenomic hybrid classifier for paediatric inflammatory bowel disease","authors":"B. Wingfield, S. Coleman, T. McGinnity, A. Bjourson","doi":"10.1109/IJCNN.2016.7727318","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727318","url":null,"abstract":"Inflammatory bowel disease (IBD) is a group of inflammatory diseases of the human colon and small intestine. IBD symptoms are non-specific; diagnosis can be delayed because an invasive colonoscopy is required for confirmation. Delayed diagnosis is linked to poor growth in children. Imbalances in the human intestinal microbiome - the community of microorganisms that reside in the human gut - are thought to contribute to the development of IBD. Work done to date in classifying host health statuses from patterns in human microbiomes with supervised learning algorithms has focused on modelling what is present in the gut (i.e. a bacterial census) with the random forest algorithm. Metagenomic shotgun sequencing is required to understand what is occurring in the gut (i.e. gene functions) and is often cost prohibitive for hundreds of samples. However, gene functions can be predicted with the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PiCRUSt) software package, which could represent a valuable source of new features. In this paper we investigate feature relevance across the feature set with the Boruta algorithm. We find that the majority of relevant features are from the predicted metagenome. Support vector machines (SVM) and multilayer perceptrons (MLP) are rarely used with microbiomic datasets but offer several theoretical advantages. To determine if the new features and alternative algorithms are appropriate, we experiment with a range of machine learning and computational intelligence algorithms. With the best performing algorithms we also implement a conditional multiple classifier system that can identify IBD presence, IBD subtype, and IBD activity from a non-invasive stool sample.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126142136","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: 12

Detection and classification of power quality disturbances in time domain using probabilistic neural network 基于概率神经网络的电能质量时域扰动检测与分类

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727344

Ziming Chen, Mengshi Li, T. Ji, Qinghua Wu

{"title":"Detection and classification of power quality disturbances in time domain using probabilistic neural network","authors":"Ziming Chen, Mengshi Li, T. Ji, Qinghua Wu","doi":"10.1109/IJCNN.2016.7727344","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727344","url":null,"abstract":"This paper proposes a new approach for detection and classification of power quality (PQ) disturbances in time domain. Most research in this field employs frequency domain analysis tools to analyse the features of PQ disturbances, such as Fourier transform and wavelet transform. However, the transient and steady-state characteristics of PQ disturbances are originally reflected on the waveforms of PQ disturbances, i.e., in time domain. In order to detect and classify the PQ disturbances in time domain, mathematical morphology (MM) and Teager energy operator (TEO), which are excellent analysis tools in time domain, are used for feature extraction in this paper. The features compose a feature vector. After that, a probabilistic neural network (PNN), which is more effective as a classifier than other neural network, is used to classify PQ disturbance signals. The feature vector composed of features extracted by MM and TEO is considered as the input of PNN. The proposed approach is tested by PQ disturbance signals, which are simulated according to the IEEE 1159-2009 standard, including swell, sag, interruption, harmonics, notching, oscillatory, fluctuation, and several combinations of these disturbances. The results demonstrate that the features extracted by MM and TEO are effective and the PNN classifies PQ disturbances with high accuracy rate.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"325 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124594322","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: 13

Evaluation of machine learning methods to predict knee loading from the movement of body segments 从身体部分的运动预测膝关节负荷的机器学习方法评估

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727882

A. Aljaaf, A. Hussain, P. Fergus, Andrzej Przybyla, G. Barton

{"title":"Evaluation of machine learning methods to predict knee loading from the movement of body segments","authors":"A. Aljaaf, A. Hussain, P. Fergus, Andrzej Przybyla, G. Barton","doi":"10.1109/IJCNN.2016.7727882","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727882","url":null,"abstract":"Abnormal joint moments during gait are validated predictors of knee pain in osteoarthritis. Calculation of moments necessitates measurement of forces and moment arms about joints during walking. Dynamically changing moment arms can be calculated from motion trackers either optically or with wireless inertia sensing units, but the measurement of forces is more problematic. Either the patient has to walk over a force platform or a force sensing device has to be built into the sole of the shoes. One possible means of registering abnormal joint moments without the restrictions due to force measurements is to predict moments from the movement of body segments using advanced machine learning techniques. To test the viability of this approach, we aimed to predict the frontal plane internal knee abduction moment form 3D Euler angles of the ankle, knee, hip and pelvis during a single gait cycle of 31 patients with alkaptonuria. Four machine-learning algorithms were used in our experiment to predict moments namely: Decision Tree, Random Forest, Linear Regression and Multilayer Perceptron neural network. Based on performance measures of prediction (R2, root mean squared error and area under the recall curve), the random forest algorithm performed best but this was also the slowest by a factor of 10. Considering both performance and speed, the Multilayer Perceptron neural network method was superior with R2, root mean square of error, area under the recall curve and required training time of 0.8616, 0.0743, 0.874 and 730 ms, respectively.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124842790","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: 27

Spatiotemporal saliency based on location prior model 基于位置先验模型的时空显著性研究

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727514

Liuyi Hu, Zhongyuan Wang, Mang Ye, Jing Xiao, R. Hu

{"title":"Spatiotemporal saliency based on location prior model","authors":"Liuyi Hu, Zhongyuan Wang, Mang Ye, Jing Xiao, R. Hu","doi":"10.1109/IJCNN.2016.7727514","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727514","url":null,"abstract":"Saliency detection for images and videos becomes increasingly popular due to its wide applicability. Enormous research efforts have been focused on saliency detection, but it still has some issues in maintaining spatiotemporal consistency of videos and uniformly highlighting entire objects. To address these issues, this paper proposes a superpixel-level spatiotemporal saliency model for saliency detection in videos. To detect salient object, we extract multiple spatiotemporal features combined with intra-consistency motion information preliminarily. Meanwhile, considering inter-consistency of foreground in videos, a set of foreground locations are obtained from previous frames. Then, we introduce foreground-background and local foreground contrast saliency cues of those features using the location prior information of foreground. These two improved contrast saliency cues uniformly highlight the entire object and suppress the background effectively. Finally, we use an interactively dynamic fusion method to integrate the output spatial and temporal saliency maps. The proposed approach is validated on challenging sets of video sequences. Subjective observations and objective evaluations demonstrate that the proposed model achieves a better performance on saliency detection compared with the state-of-the-art spatiotemporal saliency methods.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124868951","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

Clustering based methods for solar power forecasting 基于聚类的太阳能发电预测方法

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727374

Zheng Wang, I. Koprinska, Mashud Rana

引用次数: 13

Deep neural networks for Multi-Room Voice Activity Detection: Advancements and comparative evaluation 用于多房间语音活动检测的深度神经网络:进展和比较评价

2016 International Joint Conference on Neural Networks (IJCNN) Pub Date : 2016-07-24 DOI: 10.1109/IJCNN.2016.7727633

Fabio Vesperini, Paolo Vecchiotti, E. Principi, S. Squartini, F. Piazza

{"title":"Deep neural networks for Multi-Room Voice Activity Detection: Advancements and comparative evaluation","authors":"Fabio Vesperini, Paolo Vecchiotti, E. Principi, S. Squartini, F. Piazza","doi":"10.1109/IJCNN.2016.7727633","DOIUrl":"https://doi.org/10.1109/IJCNN.2016.7727633","url":null,"abstract":"This paper focuses on Voice Activity Detectors (VAD) for multi-room domestic scenarios based on deep neural network architectures. Interesting advancements are observed with respect to a previous work. A comparative and extensive analysis is lead among four different neural networks (NN). In particular, we exploit Deep Belief Network (DBN), Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory recurrent neural network (BLSTM) and Convolutional Neural Network (CNN). The latter has recently encountered a large success in the computational audio processing field and it has been successfully employed in our task. Two home recorded datasets are used in order to approximate real-life scenarios. They contain audio files from several microphones arranged in various rooms, from whom six features are extracted and used as input for the deep neural classifiers. The output stage has been redesigned compared to the previous author's contribution, in order to take advantage of the networks discriminative ability. Our study is composed by a multi-stage analysis focusing on the selection of the features, the network size and the input microphones. Results are evaluated in terms of Speech Activity Detection error rate (SAD). As result, a best SAD equal to 5.8% and 2.6% is reached respectively in the two considered datasets. In addiction, a significant solidity in terms of microphone positioning is observed in the case of CNN.","PeriodicalId":109405,"journal":{"name":"2016 International Joint Conference on Neural Networks (IJCNN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128166942","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: 19