2020 International Conference on Data Mining Workshops (ICDMW)最新文献_第4页

Multi-Task Time Series Forecasting With Shared Attention 具有共同关注的多任务时间序列预测

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00132

Zekai Chen, Jiaze E, Xiao Zhang, Hao Sheng, Xiuzhen Cheng

引用次数: 9

The IEEE ICDM 2020 Workshops IEEE ICDM 2020研讨会

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00009

G. D. Fatta, V. Sheng, A. Cuzzocrea

引用次数: 1

Textual Lyrics Based Emotion Analysis of Bengali Songs 基于文本歌词的孟加拉语歌曲情感分析

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00015

Devjyoti Nath, Anirban Roy, Sumitra Kumari Shaw, Amlan Ghorai, Shanta Phani

引用次数: 1

Copyright 版权

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/icdmw51313.2020.00003

引用次数: 0

CAMTA: Causal Attention Model for Multi-touch Attribution 多点触控归因的因果注意模型

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00020

Sachin Kumar, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, L. Vig, Gautam M. Shroff

{"title":"CAMTA: Causal Attention Model for Multi-touch Attribution","authors":"Sachin Kumar, Garima Gupta, Ranjitha Prasad, Arnab Chatterjee, L. Vig, Gautam M. Shroff","doi":"10.1109/ICDMW51313.2020.00020","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00020","url":null,"abstract":"Advertising channels have evolved from conventional print media, billboards and radio-advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advertisers revisit the design of ad campaigns to concurrently serve the requirements emerging out of new ad channels, it is also critical for advertisers to estimate the contribution from touch-points (view, clicks, converts) on different channels, based on the sequence of customer actions. This process of contribution measurement is often referred to as multi-touch attribution (MTA). In this work, we propose CAMTA, a novel deep recurrent neural network architecture which is a causal attribution mechanism for user-personalised MTA in the context of observational data. CAMTA minimizes the selection bias in channel assignment across time-steps and touchpoints. Furthermore, it utilizes the users' pre-conversion actions in a principled way in order to predict per-channel attribution. To quantitatively benchmark the proposed MTA model, we employ the real-world Criteo dataset and demonstrate the superior performance of CAMTA with respect to prediction accuracy as compared to several baselines. In addition, we provide results for budget allocation and user-behaviour modeling on the predicted channel attribution.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133179011","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

Deep Cooperative Reconstruction with Security Constraints in multi-view environments 多视图环境下具有安全约束的深度协同重构

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00083

D. Maurel, Sylvain Lefebvre, Jérémie Sublime

{"title":"Deep Cooperative Reconstruction with Security Constraints in multi-view environments","authors":"D. Maurel, Sylvain Lefebvre, Jérémie Sublime","doi":"10.1109/ICDMW51313.2020.00083","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00083","url":null,"abstract":"Nowadays, we can observe a multiplication of multiview data in domains such as marketing, bank administration, survey analysis, or social networks: We are dealing with large data bases that share a fair amount of data representing the same individual with different features depending on the data base. In this context, one can use Machine Learning methods to analyze this fragmented data across several heterogeneous sources (called views). Such analysis is subject to several difficulties: First, not all individual will be present and represented in all data sites and views. And second, this type of cross site analysis raises several ethical questions on privacy issues as no local site should have direct access to data from the other sources. To solve these problems, we present a method called the Cooperative Reconstruction System which aims at reconstructing information missing in some views in a multi-view context using information available in the other views. Furthermore, our method considers privacy issues and therefore achieves said reconstruction without direct data transfer from one view to another.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133800420","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

Analysis of multivariate time series predictability based on their features 基于多变量时间序列特征的可预测性分析

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00055

A. Kovantsev, P. Gladilin

{"title":"Analysis of multivariate time series predictability based on their features","authors":"A. Kovantsev, P. Gladilin","doi":"10.1109/ICDMW51313.2020.00055","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00055","url":null,"abstract":"In this study we explore the features of time-series that can be used for evaluation of their predictability. We suggest using features based on Kolmogorov-Sinai entropy, correlation dimension and Hurst exponent to test multivariate predictability. Besides we use two new features such as ‘noise measure’ and ‘random walk detection’. Then we experimentally test the accuracy of multivariate time series forecasting models, including vector autoregressive model (VAR), multivariate singular spectrum analysis (MSSA) model, local approximation (LA) model and recurrent neural network model with long short term memory (LSTM) cells. At last we test different causality methods for choosing additional time series as the predictors and claim that the relevance of taking into account additional predictors highly depends on the characteristics of the target time series and can be estimated using the developed method. The results of the work can be used as theoretical and experimental basis for the development of forecasting applications for the short time series using a combination of corporate and open source data as additional data predictors.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129423054","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

Efficient Distance-based Global Sensitivity Analysis for Terrestrial Ecosystem Modeling 基于距离的陆地生态系统模拟全球敏感性分析

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00052

D. Lu, D. Ricciuto

{"title":"Efficient Distance-based Global Sensitivity Analysis for Terrestrial Ecosystem Modeling","authors":"D. Lu, D. Ricciuto","doi":"10.1109/ICDMW51313.2020.00052","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00052","url":null,"abstract":"Sensitivity analysis in terrestrial ecosystem modeling is important for understanding controlling processes, guiding model development, and targeting new observations to reduce parameter and prediction uncertainty. Complex and computationally expensive terrestrial ecosystem models (TEM) limit the number of ensemble simulations, requiring sophisticated and efficient methods to analyze sensitivities of multiple model responses to different types of parameter uncertainties. In this study, we propose a distance-based global sensitivity analysis (DGSA) method. DGSA first classifies model response samples into a small set of discrete classes and then calculates the distance between parameter frequency distributions in different classes to measure the parameter sensitivity. The principle is that, if the parameter distribution is the same in each class, then the model response is insensitive to the parameter, while a large difference in the distributions indicates the parameter is influential to the response. Built on this idea, DGSA can be applied to analyze sensitivity of a single and a group of responses to different kinds of parameter uncertainties including continuous, discrete and even stochastic. Besides the main-effect sensitivity from a single parameter, DGSA can also quantify the sensitivity from parameter interactions. Additionally, DGSA is computationally efficient which can use a small number of model evaluations to obtain an accurate and statistically significant result. We applied DGSA to two TEMs, one having eight parameters and three kinds of model responses, and the other having 47 parameters and a long-period response. We demonstrated that DGSA can be used for sensitivity problems with multiple responses and high-dimensional parameters efficiently.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131333939","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

Rebuilding Trust in Active Learning with Actionable Metrics 用可操作的指标重建主动学习中的信任

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00120

A. Abraham, L. Dreyfus-Schmidt

引用次数: 4

You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters 你看到的是一组马车，而我看到的是一列火车:朝着局部和全局任意定向子空间集群的统一视图前进

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00050

Daniyal Kazempour, Long Matthias Yan, Peer Kröger, T. Seidl

{"title":"You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters","authors":"Daniyal Kazempour, Long Matthias Yan, Peer Kröger, T. Seidl","doi":"10.1109/ICDMW51313.2020.00050","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00050","url":null,"abstract":"Having data with a high number of features raises the need to detect clusters which exhibit within subspaces of features a high similarity. These subspaces can be arbitrarily oriented which gave rise to arbitrarily-oriented subspace clustering (AOSC) algorithms. In the diversity of such algorithms some are specialized at detecting clusters which are global, across the entire dataset regardless of any distances, while others are tailored at detecting local clusters. Both of these views (local and global) are obtained separately by each of the algorithms. While from an algebraic point of view, none of both representations can claim to be the true one, it is vital that domain scientists are presented both views, enabling them to inspect and decide which of the representations is closest to the domain specific reality. We propose in this work a framework which is capable to detect locally dense arbitrarily oriented subspace clusters which are embedded within a global one. We also first introduce definitions of locally and globally arbitrarily oriented subspace clusters. Our experiments illustrate that this approach has no significant impact on the cluster quality nor on the runtime performance, and enables scientists to be no longer limited exclusively to either of the local or global views.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116709168","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