2014 IEEE 26th International Conference on Tools with Artificial Intelligence最新文献_第7页

HP Trend Filtering Using Gaussian Mixture Model Weighted Heuristic 基于高斯混合模型加权启发式的HP趋势滤波

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.150

L. Sayfullina, Magnus Westerlund, Kaj-Mikael Björk, H. Toivonen

{"title":"HP Trend Filtering Using Gaussian Mixture Model Weighted Heuristic","authors":"L. Sayfullina, Magnus Westerlund, Kaj-Mikael Björk, H. Toivonen","doi":"10.1109/ICTAI.2014.150","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.150","url":null,"abstract":"Trends show the underlying structure of the time series data. Trend estimation is a commonly used tool for financial market movement prediction. In traditional approaches, such as Hodrick-Prescott (HP) and L1 filtering, the trend is considered as a smoothed version of the time-series, including rare significant hills that are smoothed in the same way as usual noise. The goal of this paper is to allow the estimated trend to be more complex and detailed in the intervals of significant changes while making a smooth estimate in all other parts. This will be our main criteria for trend estimation. We present a modified version of HP weighted heuristic that provides the best trend according to the abovementioned criteria. Gaussian Mixture Models (GMMs) on the preliminary estimated trend are used in the weighted HP heuristic to decrease the penalty in the objective function for turning-point intervals. We conducted a set of experiments on financial datasets and compared the results with those obtained from the standard HP filtering with weighted heuristic. The results indicate an improvement in the cycling component using our proposed criteria compared to the HP filtering approach.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121548309","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

Reframing Continuous Input Attributes 重构连续输入属性

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.16

Chowdhury Farhan Ahmed, N. Lachiche, Clément Charnay, Agnès Braud

{"title":"Reframing Continuous Input Attributes","authors":"Chowdhury Farhan Ahmed, N. Lachiche, Clément Charnay, Agnès Braud","doi":"10.1109/ICTAI.2014.16","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.16","url":null,"abstract":"Reuse of learnt knowledge is of critical importance in the majority of knowledge-intensive application areas, particularly because the operating context can be expected to vary from training to deployment. Dataset shift is a crucial example of this where training and testing datasets follow different distributions. However, most of the existing dataset shift solving algorithms need costly retraining operation and are not suitable to use the existing model. In this paper, we propose a new approach called reframing to handle dataset shift. The main objective of reframing is to build a model once and make it workable without retraining. We propose two efficient reframing algorithms to learn the optimal shift parameter values using only a small amount of labelled data available in the deployment. Thus, they can transform the shifted input attributes with the optimal parameter values and use the same existing model in several deployment environments without retraining. We have addressed supervised learning tasks both for classification and regression. Extensive experimental results demonstrate the efficiency and effectiveness of our approach compared to the existing solutions. In particular, we report the existence of dataset shift in two real-life datasets. These real-life unknown shifts can also be accurately modeled by our algorithms.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117093347","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

A Supervised Feature Selection Algorithm through Minimum Spanning Tree Clustering 基于最小生成树聚类的有监督特征选择算法

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.47

Qin Liu, Jingxiao Zhang, Jiakai Xiao, Hongming Zhu, Qinpei Zhao

引用次数: 12

XML Document Co-clustering via Non-negative Matrix Tri-factorization 基于非负矩阵三因子分解的XML文档共聚类

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.96

G. Costa, R. Ortale

{"title":"XML Document Co-clustering via Non-negative Matrix Tri-factorization","authors":"G. Costa, R. Ortale","doi":"10.1109/ICTAI.2014.96","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.96","url":null,"abstract":"XML co-clustering is a promising method to overcome the effectiveness of traditional XML clustering approaches, due to the exploitation of the mutual relationships between XML documents and their respective XML features while clustering both simultaneously. To shed light on this so far unexplored research direction, we conduct a systematic study of the effectiveness of XML co-clustering, by viewing the task as parametric with respect to the XML features. Thus, the definition and exploitation of three distinct types of XML features, which are respectively informative of the content, structure and both aspects of the XML documents, allows an in-depth investigation of all three different instances of the XML co-clustering task, i.e., XML co-clustering by content alone, structure alone as well as both structure and content. XML co-clustering relies on a non-negative matrix trifactorization technique, that efficiently processes large-scale input data, which is especially useful with large corpora of text-centric XML documents. The relevance of the structural and content features of the XML documents is assessed through a new weighting scheme. An intensive experimental evaluation on real-world benchmark XML corpora reveals a higher effectiveness of XML co-clustering in comparison with state-of-the-art approaches to XML clustering. Insights are also provided on the effectiveness of XML feature clustering.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115971233","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: 10

Removed Set-Based Revision of Abstract Argumentation Frameworks 删除了基于集合的抽象论证框架修订

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.121

Farid Nouioua, Éric Würbel

引用次数: 10

Sentiment Analysis: Towards a Tool for Analysing Real-Time Students Feedback 情感分析:一个分析实时学生反馈的工具

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.70

Nabeela Altrabsheh, Ella Haig, Sanaz Fallahkhair

引用次数: 69

Estimating Upper Bounds for Improving the Filtering in Interval Branch and Bound Optimizers 区间分支和有界优化器中改进滤波的上界估计

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.15

Ignacio Araya

引用次数: 0

Automated Model Revision for Coordinated Open Systems 协调开放系统的自动模型修正

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.149

Jun Wu, Chong-Jun Wang, Junyuan Xie

引用次数: 0

Community Detection in Multidimensional Networks 多维网络中的社区检测

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.60

Alessia Amelio, C. Pizzuti

引用次数: 38

A Generic Algorithmic Framework to Solve Special Versions of the Set Partitioning Problem 一种解决特殊版本集划分问题的通用算法框架

2014 IEEE 26th International Conference on Tools with Artificial Intelligence Pub Date : 2014-11-10 DOI: 10.1109/ICTAI.2014.136

Robin Lamarche-Perrin, Y. Demazeau, J. Vincent

{"title":"A Generic Algorithmic Framework to Solve Special Versions of the Set Partitioning Problem","authors":"Robin Lamarche-Perrin, Y. Demazeau, J. Vincent","doi":"10.1109/ICTAI.2014.136","DOIUrl":"https://doi.org/10.1109/ICTAI.2014.136","url":null,"abstract":"Given a set of individuals, a collection of subsets, and a cost associated to each subset, the Set Partitioning Problem (SPP) consists in selecting some of these subsets to build a partition of the individuals that minimizes the total cost. This combinatorial optimization problem has been used to model dozens of problems arising in specific domains of Artificial Intelligence and Operational Research, such as coalition structures generation, community detection, multilevel data analysis, workload balancing, image processing, and database optimization. All these applications are actually interested in special versions of the SPP where assumptions regarding the admissible subsets constraint the search space and allow tractable optimization algorithms. However, there is a major lack of unity regarding the identification, the formalization, and the resolution of these strongly-related problems. This paper hence proposes a generic framework to design dynamic programming algorithms that fit with the particular algebraic structure of special versions of the SPP. We show how this framework can be applied to two well-known versions, thus opening a unified approach to solve new ones that might arise in the future.","PeriodicalId":142794,"journal":{"name":"2014 IEEE 26th International Conference on Tools with Artificial Intelligence","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123384070","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: 11