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检索条件"任意字段=18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017"

共 91 条记录，以下是61-70 订阅

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Cost-Sensitive Alternating Direction Method of Multipliers for Large-Scale Classification 18th

Cost-Sensitive Alternating Direction Method of Multipliers f...

引用

18th international conference on intelligent data engineering and automated learning (ideal)

作者： Wang, Huihui Shi, Yinghuan Chen, Xingguo Gao, Yang Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci & Technol Sch Software Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783319689357;9783319689340

Large-scale classification is one of the most significant topics in machine learning. However, previous classification methods usually require the assumption that the data has a balanced class distribution. thus, when dealing with imbalanced data, these methods often present performance degradation. In order to seek the better performance in large-scale classification, we propose a novel Cost-Sensitive Alternating Direction Method of Multipliers method (CSADMM) to deal with imbalanced data in this paper. CSADMM derives the problem into a series of subproblems efficiently solved by a dual coordinate descent method in parallel. In particular, CSADMM incorporates different classification costs for large-scale imbalanced classification by cost-sensitive learning. Experimental results on several large-scale imbalanced datasets show that compared with distributed random forest and fuzzy rule based classification system, CSADMM obtains better classification performance, with the training time is significantly reduced. Moreover, compared with single-machine methods, CSADMM also shows promising results.

关键词： Large-scale classification Cost-sensitive learning Alternating direction method of multipliers Imbalanced data

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Heterogeneous Context-aware Recommendation Algorithm with Semi-supervised Tensor Factorization 18th

Heterogeneous Context-aware Recommendation Algorithm with Se...

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Cai, Guoyong Gu, Weidong Guilin Univ Elect Technol Guilin Peoples R China Guangxi Key Lab Trusted Software Guangxi Peoples R China

ISBN: (纸本)9783319689357;9783319689340

data sparsity is one of the most challenging problems in recommender systems. In this paper, we tackle the data sparsity problem by proposing a heterogeneous context-aware semi-supervised tensor factorization method named HASS. Firstly, heterogeneous context are classified and processed by different modeling approaches. We use a tensor factorization model to capture user-item interaction contexts and use a matrix factorization model to capture both user attributed contexts and item attributed contexts. Secondly, different context models are optimized with semi-supervised co-training approach. Finally, the two sub-models are combined effectively by an weight fusing method. As a result, the HASS method has several distinguished advantages for mitigating the data sparsity problem. One is that the method can well perceive diverse influences of heterogeneous contexts and another is that a large number of unlabeled samples can be utilized by the co-training stage to further alleviate the data sparsity problem. the proposed algorithm is evaluated on real-world datasets and the experimental results show that HASS model can significantly improve recommendation accuracy by comparing with the existing state-of-art recommendation algorithms.

关键词： Recommender systems Heterogeneous Context-aware data sparsity Semi-supervised learning

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Face Anti-spoofing Algorithm Based on Gray Level Co-occurrence Matrix and Dual Tree Complex Wavelet Transform 18th

Face Anti-spoofing Algorithm Based on Gray Level Co-occurren...

引用

18th international conference on intelligent data engineering and automated learning (ideal)

作者： Qu, Xiaofeng Li, Hengjian Dong, Jiwen Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Shandong Peoples R China

ISBN: (纸本)9783319689357;9783319689340

By analyzing the difference of facial texture features between living face and photo, we propose a novel face anti-spoofing algorithm based on gray level co-occurrence matrix (GLCM) and dual-tree complex wavelet tree (DT-CWT). Firstly, inspired by the co-occurrence matrix, we extract five texture features including angle second moment, entropy, contrast, correlation and local uniformity to represent the gray direction, interval and amplitude information for the face texture information. Secondly, DT-CWT has the advantages of approximate translation invariance and good direction selectivity. therefore, the coefficients of DT-CWT can enhance the texture information and edge information in the frequency domain. At last, the SVM classification is used to distinguish between true and fake face. Our algorithm is demonstrated on the published NUAA database. Compared with the existing methods, the feature dimension is reduced. the experimental results show that the proposed algorithm improves the detection accuracy.

关键词： Anti-spoofing Liveness detection Gray level co-occurrence matrix Dual tree complex wavelet transform

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Semi-supervised Regularized Discriminant Analysis for EEG-Based BCI System 18th

Semi-supervised Regularized Discriminant Analysis for EEG-Ba...

引用

18th international conference on intelligent data engineering and automated learning (ideal)

作者： Xin, Yuhang Wu, Qiang Zhao, Qibin Wu, Qi Shandong Univ Sch Informat Sci & Engn Jinan 250010 Shandong Peoples R China RIKEN Ctr Adv Intelligence Project Tensor Learning Unit Chuo Ku Tokyo 1030027 Japan Shandong Univ Qilu Hosp Jinan 250012 Shandong Peoples R China

ISBN: (纸本)9783319689357;9783319689340

Brain-Computer interface (BCI) is a new technique which allows direction connection between human and computer or other external device. It employs the classification of event-related potential to control the equipment rather than using language or limb movement. Currently, one of the most important issues for ERP-based BCIs is that the ERP classification performance degrades when the training number of samples is small. In order to solve this problem, semi-supervised regularized discriminant analysis (SRDA) was proposed to extract features and classify ERP patterns by integrating semi-supervised learning and regularization approach. the labeled data was used to maximize the separability between different classes and calculate the within-class covariance matrix by regularization. the labeled and unlabeled data were employed to construct the penalty term by neighbor graph. Our proposed approach was evaluated on the BCI Competition Challenge dataset and the simulation results indicated that it achieved a better accuracy than the traditional algorithms.

关键词： Brain computer interface

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Interactive data Visualization Using Dimensionality Reduction and Dissimilarity-Based Representations 18th

Interactive Data Visualization Using Dimensionality Reductio...

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Pena-Unigarro, D. F. Rosero-Montalvo, P. Revelo-Fuelagan, E. J. Castro-Silva, J. A. Alvarado-Perez, J. C. theron, R. Ortega-Bustamante, C. M. Peluffo-Ordonez, D. H. Univ Narino Pasto Colombia Univ Tecn Norte Ibarra Ecuador Inst Tecnol Super 17 Julio Ibarra Ecuador Univ Surcolombiana Neiva Colombia Coorporac Univ Autonoma Narino Pasto Colombia Univ Salamanca Salamanca Spain

ISBN: (纸本)9783319689357;9783319689340

this work describes a new model for interactive data visualization followed from a dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting spaces of DR methods is considered, which is carried out by a weighted sum. For the sake of user interaction, corresponding weighting factors are given via an intuitive color-based interface. Also, to depict the DR outcomes while showing information about the input high-dimensional data space, the low-dimensional representations reached by the mixture is conveyed using scatter plots enhanced with an interactive data-driven visualization. In this connection, a constrained dissimilarity approach define the graph to be drawn on the scatter plot.

关键词： data visualization Dimensionality reduction Pairwise dissimilarity

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Convolutional Neural Networks for Unsupervised Anomaly Detection in Text data 18th

Convolutional Neural Networks for Unsupervised Anomaly Detec...

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Gorokhov, Oleg Petrovskiy, Mikhail Mashechkin, Igor Lomonosov Moscow State Univ MSU Dept Comp Sci Moscow 119899 Russia

ISBN: (纸本)9783319689357;9783319689340

In this paper, we discuss the problem of anomaly detection in text data using convolutional neural network (CNN). Recently CNNs have become one of the most popular and powerful tools for various machine learning tasks. CNN's main advantage is an ability to extract complicated hidden features from high dimensional data with complex structure. Usually CNNs are applied in supervised learning mode. On the other hand, unsupervised anomaly detection is an important problem in many applications, including computer security, behavioral analytics, etc. Since there is no specified target in unsupervised mode, traditional CNN's objective functions cannot be used. In this paper, we develop a specific CNN architecture. It consists of one convolutional layer and one subsampling layer, we use RBF activation function and logarithmic loss function on the final layer. Minimization of the corresponding objective function helps us to calculate the location parameter of the features' weights discovered on the last network layer. We use l(2)-regularization to avoid degenerate solution. Proposed CNN has been tested on anomalies discovering in a stream of text documents modeled with well-known Enron dataset, where proposed method demonstrates better results in comparison with the traditional outlier detection methods based on one-class SVM and NMF.

关键词： Anomaly detection Text mining Convolutional neural network One-class classification Regularization SVM NMF

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Dynamic Community Detection Algorithm Based on Automatic Parameter Adjustment 1

引用

18th international conference on intelligent data engineering and automated learning (ideal)

作者： Lu, Kai Wang, Xin Wang, Xiaoping Natl Univ Def Technol Coll Comp Sci Changsha Hunan Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China

ISBN: (数字)9783319689357

ISBN: (纸本)9783319689357;9783319689340

Community detection is widely used in social network analysis. It clusters densely connected vertices into communities. As social networks get larger, scalable algorithms are drawing more attention. Among those methods, the algorithm named Attractor is quite outstanding both in terms of accuracy and scalability. However, it is highly dependent on the parameter, which is abstract for users. the improper parameter value can bring about some problems. there can be a huge community (monster) sometimes;other time the communities are generally too small (fragments). the existing fragments also need eliminating. Such phenomenon greatly deteriorates the performance of Attractor. We modify the algorithm and propose mAttractor, which adjusts the parameter automatically. We introduce two constraints to limit monsters and fragments and to narrow the parameter range. An optional parameter is also introduced. the proposed algorithm can choose to satisfy or ignore the optional parameter by judging whether it is reasonable. Our algorithm also eliminates the existing fragments. A delicate pruning is designed for fast determination. Experiments show that our mAttractor outperforms Attractor by 2%-270%.

关键词： Community detection Social network data mining

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learning Convolutional Ranking-Score Function by Query Preference Regularization 1

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Zhang, Guohui Liang, Gaoyuan Li, Weizhi Fang, Jian Wang, Jingbin Geng, Yanyan Wang, Jing-Yan Huawei Technol Co Ltd Shanghai Peoples R China Jiangsu Univ Technol Nanjing 213001 Jiangsu Peoples R China Reflektion Inc San Mateo CA 94404 USA Auburn Univ Auburn AL 36849 USA Intermediate Peoples Court Linyi City Informat Technol Serv Ctr Linyi Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Atmospher Environm & Equip Jiangsu Key Lab Big Data Anal Technol B DAT Nanjing Jiangsu Peoples R China

ISBN: (数字)9783319689357

ISBN: (纸本)9783319689357;9783319689340

Ranking score plays an important role in the system of content-based retrieval. Given a query, the database items are ranked according to the ranking scores in a descending order, and the top-ranked items are returned as retrieval results. In this paper, we propose a new ranking scoring function based on the convolutional neural network (CNN). the ranking scoring function has a structure of CNN, and its parameters are adjusted to both queries and query preferences. the learning process guarantees that the ranking score of the query itself is large, and also the ranking scores of the positives (database items which the query wants to link) are larger than those of the negatives (database items which the query wants to avoid). Moreover, we also impose that the neighboring database items have similar ranking scores. An optimization problem is formulated and solved by Estimation-Maximization method. Experiments over the benchmark data sets show the advantage over the existing learning-to-rank methods.

关键词： Convolutional neural network Content-based retrieval Ranking score Estimation-maximization method

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Fresh-Phish: A Framework for Auto-Detection of Phishing Websites 18

Fresh-Phish: A Framework for Auto-Detection of Phishing Webs...

引用

IEEE 18th international conference on Information Reuse and Integration (IRI)

作者： Shirazi, Hossein Haefner, Kyle Ray, Indrakshi Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA

ISBN: (纸本)9781538615621

Denizens of the Internet are coming under a barrage of phishing attacks of increasing frequency and sophistication. Emails accompanied by authentic looking websites are ensnaring users who, unwittingly, hand over their credentials compromising both their privacy and security. Methods such as the blacklisting of these phishing websites become untenable and cannot keep pace with the explosion of fake sites. Detection of nefarious websites must become automated and be able to adapt to this ever evolving form of social engineering. We develop a framework, called "Fresh-Phish", for creating current machine learning data for phishing websites. Using 30 different website features that we query using python, we build a large labeled dataset and analyze several machine learning classifiers against this dataset to determine which is the most accurate. We analyze not just the accuracy of the technique, but also how long it takes to train the model.

关键词： Phishing Machine learning TensorFlow

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Long Short-Term Memory Networks for Automatic Generation of Conversations 18

Long Short-Term Memory Networks for Automatic Generation of ...

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18th IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Fujita, Tomohiro Bai, Wenjun Quan, Changqin Kobe Univ Grad Sch Syst Informat Dept Computat Sci Kobe Hyogo Japan

ISBN: (纸本)9781509055043

Human Machine Interface demands the communicative propriety that would be applied in various linguistic tasks. In this research, we develop an intelligent 'chat bot', which generates conversational sentences via recurrent neural network and its coupled memory unit, long short-term memory (LSTM). Word strings in conversations are considered as time series data. Using a single neural network model that performs a simple task of outputting the next word from the preceding word, a conversational sentence can be generated by connecting the words. In the experiment, we performed the linguistic 'Turning Test' to evaluate the proposed system.

关键词： Conversation Generation Deep-learning Long Short Term Memory (LSTM) Japanese tweets

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