International Conference on Neural Information Processing最新文献_第4页

Impact of the composition of feature extraction and class sampling in medicare fraud detection 特征提取和类别抽样组合在医疗欺诈检测中的影响

International Conference on Neural Information Processing Pub Date : 2022-06-03 DOI: 10.48550/arXiv.2206.01413

Akrity Kumari, N. Punn, S. K. Sonbhadra, Sonali Agarwal

{"title":"Impact of the composition of feature extraction and class sampling in medicare fraud detection","authors":"Akrity Kumari, N. Punn, S. K. Sonbhadra, Sonali Agarwal","doi":"10.48550/arXiv.2206.01413","DOIUrl":"https://doi.org/10.48550/arXiv.2206.01413","url":null,"abstract":"With healthcare being critical aspect, health insurance has become an important scheme in minimizing medical expenses. Following this, the healthcare industry has seen a significant increase in fraudulent activities owing to increased insurance, and fraud has become a significant contributor to rising medical care expenses, although its impact can be mitigated using fraud detection techniques. To detect fraud, machine learning techniques are used. The Centers for Medicaid and Medicare Services (CMS) of the United States federal government released\"Medicare Part D\"insurance claims is utilized in this study to develop fraud detection system. Employing machine learning algorithms on a class-imbalanced and high dimensional medicare dataset is a challenging task. To compact such challenges, the present work aims to perform feature extraction following data sampling, afterward applying various classification algorithms, to get better performance. Feature extraction is a dimensionality reduction approach that converts attributes into linear or non-linear combinations of the actual attributes, generating a smaller and more diversified set of attributes and thus reducing the dimensions. Data sampling is commonlya used to address the class imbalance either by expanding the frequency of minority class or reducing the frequency of majority class to obtain approximately equal numbers of occurrences for both classes. The proposed approach is evaluated through standard performance metrics. Thus, to detect fraud efficiently, this study applies autoencoder as a feature extraction technique, synthetic minority oversampling technique (SMOTE) as a data sampling technique, and various gradient boosted decision tree-based classifiers as a classification algorithm. The experimental results show the combination of autoencoders followed by SMOTE on the LightGBM classifier achieved best results.","PeriodicalId":281152,"journal":{"name":"International Conference on Neural Information Processing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133741695","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

HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System HYCEDIS:深度文档智能系统的混合信心引擎

International Conference on Neural Information Processing Pub Date : 2022-06-01 DOI: 10.48550/arXiv.2206.02628

Bao-Sinh Nguyen, Q. Tran, Tuan-Anh Dang Nguyen, D. Nguyen, H. Le

引用次数: 0

Active Learning with Weak Supervision for Gaussian Processes 高斯过程的弱监督主动学习

International Conference on Neural Information Processing Pub Date : 2022-04-18 DOI: 10.1007/978-981-99-1642-9_17

Amanda Olmin, Jakob Lindqvist, Lennart Svensson, F. Lindsten

引用次数: 1

Context-Based Deep Learning Architecture with Optimal Integration Layer for Image Parsing 基于上下文的深度学习架构与图像解析的最优集成层

International Conference on Neural Information Processing Pub Date : 2022-04-13 DOI: 10.1007/978-3-030-92270-2_25

Ranju Mandal, Basim Azam, B. Verma

引用次数: 0

An optimized hybrid solution for IoT based lifestyle disease classification using stress data 基于物联网的生活方式疾病分类优化混合解决方案，使用压力数据

International Conference on Neural Information Processing Pub Date : 2022-04-04 DOI: 10.48550/arXiv.2204.03573

Sadhana Tiwari, Sonali Agarwal

{"title":"An optimized hybrid solution for IoT based lifestyle disease classification using stress data","authors":"Sadhana Tiwari, Sonali Agarwal","doi":"10.48550/arXiv.2204.03573","DOIUrl":"https://doi.org/10.48550/arXiv.2204.03573","url":null,"abstract":"Stress, anxiety, and nervousness are all high-risk health states in everyday life. Previously, stress levels were determined by speaking with people and gaining insight into what they had experienced recently or in the past. Typically, stress is caused by an incidence that occurred a long time ago, but sometimes it is triggered by unknown factors. This is a challenging and complex task, but recent research advances have provided numerous opportunities to automate it. The fundamental features of most of these techniques are electro dermal activity (EDA) and heart rate values (HRV). We utilized an accelerometer to measure body motions to solve this challenge. The proposed novel method employs a test that measures a subject's electrocardiogram (ECG), galvanic skin values (GSV), HRV values, and body movements in order to provide a low-cost and time-saving solution for detecting stress lifestyle disease in modern times using cyber physical systems. This study provides a new hybrid model for lifestyle disease classification that decreases execution time while picking the best collection of characteristics and increases classification accuracy. The developed approach is capable of dealing with the class imbalance problem by using WESAD (wearable stress and affect dataset) dataset. The new model uses the Grid search (GS) method to select an optimized set of hyper parameters, and it uses a combination of the Correlation coefficient based Recursive feature elimination (CoC-RFE) method for optimal feature selection and gradient boosting as an estimator to classify the dataset, which achieves high accuracy and helps to provide smart, accurate, and high-quality healthcare systems. To demonstrate the validity and utility of the proposed methodology, its performance is compared to those of other well-established machine learning models.","PeriodicalId":281152,"journal":{"name":"International Conference on Neural Information Processing","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132271990","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

Shifted Chunk Encoder for Transformer Based Streaming End-to-End ASR 基于变压器流端到端ASR的移位块编码器

International Conference on Neural Information Processing Pub Date : 2022-03-29 DOI: 10.48550/arXiv.2203.15206

Fangyuan Wang, Bo Xu

{"title":"Shifted Chunk Encoder for Transformer Based Streaming End-to-End ASR","authors":"Fangyuan Wang, Bo Xu","doi":"10.48550/arXiv.2203.15206","DOIUrl":"https://doi.org/10.48550/arXiv.2203.15206","url":null,"abstract":"Currently, there are mainly three kinds of Transformer encoder based streaming End to End (E2E) Automatic Speech Recognition (ASR) approaches, namely time-restricted methods, chunk-wise methods, and memory-based methods. Generally, all of them have limitations in aspects of linear computational complexity, global context modeling, and parallel training. In this work, we aim to build a model to take all these three advantages for streaming Transformer ASR. Particularly, we propose a shifted chunk mechanism for the chunk-wise Transformer which provides cross-chunk connections between chunks. Therefore, the global context modeling ability of chunk-wise models can be significantly enhanced while all the original merits inherited. We integrate this scheme with the chunk-wise Transformer and Conformer, and identify them as SChunk-Transformer and SChunk-Conformer, respectively. Experiments on AISHELL-1 show that the SChunk-Transformer and SChunk-Conformer can respectively achieve CER 6.43% and 5.77%. And the linear complexity makes them possible to train with large batches and infer more efficiently. Our models can significantly outperform their conventional chunk-wise counterparts, while being competitive, with only 0.22 absolute CER drop, when compared with U2 which has quadratic complexity. A better CER can be achieved if compared with existing chunk-wise or memory-based methods, such as HS-DACS and MMA. Code is released.","PeriodicalId":281152,"journal":{"name":"International Conference on Neural Information Processing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127196628","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

Nearest Neighbor Classifier with Margin Penalty for Active Learning 基于边际惩罚的主动学习最近邻分类器

International Conference on Neural Information Processing Pub Date : 2022-03-17 DOI: 10.48550/arXiv.2203.09174

Yuan Cao, Zhiqiao Gao, Jie Hu

{"title":"Nearest Neighbor Classifier with Margin Penalty for Active Learning","authors":"Yuan Cao, Zhiqiao Gao, Jie Hu","doi":"10.48550/arXiv.2203.09174","DOIUrl":"https://doi.org/10.48550/arXiv.2203.09174","url":null,"abstract":"As deep learning becomes the mainstream in the field of natural language processing, the need for suitable active learning method are becoming unprecedented urgent. Active Learning (AL) methods based on nearest neighbor classifier are proposed and demonstrated superior results. However, existing nearest neighbor classifier are not suitable for classifying mutual exclusive classes because inter-class discrepancy cannot be assured by nearest neighbor classifiers. As a result, informative samples in the margin area can not be discovered and AL performance are damaged. To this end, we propose a novel Nearest neighbor Classifier with Margin penalty for Active Learning(NCMAL). Firstly, mandatory margin penalty are added between classes, therefore both inter-class discrepancy and intra-class compactness are both assured. Secondly, a novel sample selection strategy are proposed to discover informative samples within the margin area. To demonstrate the effectiveness of the methods, we conduct extensive experiments on for datasets with other state-of-the-art methods. The experimental results demonstrate that our method achieves better results with fewer annotated samples than all baseline methods.","PeriodicalId":281152,"journal":{"name":"International Conference on Neural Information Processing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128370966","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

PathSAGE: Spatial Graph Attention Neural Networks with Random Path Sampling PathSAGE:随机路径采样的空间图注意神经网络

International Conference on Neural Information Processing Pub Date : 2022-03-11 DOI: 10.1007/978-3-030-92270-2_10

Junhua Ma, Jiajun Li, Xueming Li, Xu Li

引用次数: 0

Automatic Language Identification for Celtic Texts 凯尔特文本的自动语言识别

International Conference on Neural Information Processing Pub Date : 2022-03-09 DOI: 10.48550/arXiv.2203.04831

Olha Dovbnia, Anna Wr'oblewska

{"title":"Automatic Language Identification for Celtic Texts","authors":"Olha Dovbnia, Anna Wr'oblewska","doi":"10.48550/arXiv.2203.04831","DOIUrl":"https://doi.org/10.48550/arXiv.2203.04831","url":null,"abstract":"Language identification is an important Natural Language Processing task. It has been thoroughly researched in the literature. However, some issues are still open. This work addresses the identification of the related low-resource languages on the example of the Celtic language family. This work's main goals were: (1) to collect the dataset of three Celtic languages; (2) to prepare a method to identify the languages from the Celtic family, i.e. to train a successful classification model; (3) to evaluate the influence of different feature extraction methods, and explore the applicability of the unsupervised models as a feature extraction technique; (4) to experiment with the unsupervised feature extraction on a reduced annotated set. We collected a new dataset including Irish, Scottish, Welsh and English records. We tested supervised models such as SVM and neural networks with traditional statistical features alongside the output of clustering, autoencoder, and topic modelling methods. The analysis showed that the unsupervised features could serve as a valuable extension to the n-gram feature vectors. It led to an improvement in performance for more entangled classes. The best model achieved a 98% F1 score and 97% MCC. The dense neural network consistently outperformed the SVM model. The low-resource languages are also challenging due to the scarcity of available annotated training data. This work evaluated the performance of the classifiers using the unsupervised feature extraction on the reduced labelled dataset to handle this issue. The results uncovered that the unsupervised feature vectors are more robust to the labelled set reduction. Therefore, they proved to help achieve comparable classification performance with much less labelled data.","PeriodicalId":281152,"journal":{"name":"International Conference on Neural Information Processing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134526939","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

Efficient Policy Generation in Multi-agent Systems via Hypergraph Neural Network 基于超图神经网络的多智能体系统高效策略生成

International Conference on Neural Information Processing Pub Date : 2022-03-07 DOI: 10.1007/978-3-031-30108-7_19

Bin Zhang, Yunru Bai, Zhiwei Xu, Dapeng Li, Guoliang Fan

引用次数: 2