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Sparse weighted naive bayes classifier for efficient classification of categorical data 3

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Sparse weighted naive bayes classifier for efficient classif...

3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018

作者： Zheng, Zhuoyuan Cai, Yunpeng Yang, Yujie Li, Ye Guangxi Key Laboratory of Trusted Software Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic Technology Guilin541004 China Shenzhen Institutes of Advanced Technology Key Laboratory for Biomedical Informatics and Health Engineering Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781538642108

Feature selection has become a key challenge in machine learning with the rapid growth of data size in real-world applications. However, existing feature selection methods mainly focus on numeric data, which will lead to quality loss when handling classification problems involving categorical variables. In this paper, we proposed an improvement of Bayesian Classifier with the sparse regression technology. To the best of our knowledge, this is the first attempt to extend sparse regression for directly process of categorical variables. We implemented the idea for the case of weighted naive Bayes classifier. The introduction of L1 regularized learning ensures the algorithm to retain only a minimal subset of variables for model building, while at the same time achieves a near-optimal decision hyper-plane, which leads to excellent performance in case of high dimensional or small sample size situations. We carried out benchmark test on five UCI benchmark categorical data sets, which proved that the proposed algorithm have competitive performances over the original weighted naive bayes classifier and several state-of-the-art feature selection methods including L1 logistic regression and SVM-RFE. © 2018 IEEE.

关键词： Feature Selection

WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

iSplit LBI: Individualized partial ranking with ties via split LBI

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

On Autonomous Systems: From Reflexive, Imperative and Adaptive Intelligence to Autonomous and Cognitive Intelligence

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On Autonomous Systems: From Reflexive, Imperative and Adapti...

IEEE International Conference on Cognitive Informatics

作者： Yingxu Wang Konstantinos N. Plataniotis Sam Kwong Henry Leung Svetlana Yanushkevich Fakhri Karray Ming Hou Newton Howard Rodolfo A. Fiorini Paolo Soda Edward Tunstel Jianmin Wang Shushma Patel International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary Canada University of Toronto Canada City University of Hong Kong Hong Kong University of Waterloo Canada Toronto Research Centre DRDC Canada Computational Neurosciences Lab University of Oxford UK Information & Bioengg. Politecnico di Milano University Italy Univ. Campus Bio-Medico of Rome Italy Autonomous and Intelligent Systems President IEEE SMC School of Software Tsinghua University China School of Engineering London South Bank University UK

ISBN: (数字)9781728114194

ISBN: (纸本)9781728104966

Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and technologies of autonomous systems put emphases on human-system interactions and humans in-the-loop. This paper explores the intelligence and system foundations of autonomous systems. It focuses on what structural and behavioral properties constitute the intelligence power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A Hierarchical Intelligence Model (HIM) is introduced to elab.rate the evolution of human and system intelligence as an inductive process. A set of properties of system autonomy is formally analyzed towards a wide range of autonomous system applications in computational intelligence and systems engineering.

关键词： Frequency modulation Electronic mail Handheld computers Cognitive systems Robots Animals

Lingke: A fine-grained multi-turn chatbot for customer service

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arXiv 2018年

作者： Zhu, Pengfei Zhang, Zhuosheng Li, Jiangtong Huang, Yafang Zhao, Hai School of Computer Science and Software Engineering East China Normal University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China College of Zhiyuan Shanghai Jiao Tong University China

Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a finegrained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations. Copyright © 2018, The Authors. All rights reserved.

关键词： Supervised learning

BALSON: BAYESIAN LEAST SQUARES OPTIMIZATION WITH NONNEGATIVE L1-NORM CONSTRAINT

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BALSON: BAYESIAN LEAST SQUARES OPTIMIZATION WITH NONNEGATIVE...

IEEE Workshop on Machine Learning for Signal Processing

作者： Jiyang Xie Zhanyu Ma Guoqiang Zhang Jing-Hao Xue Jen-Tzung Chien Zhiqing Lin Jun Guo Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications China School of Computing and Communications University of Technology Sydney Australia Department of Statistical Science University College London United Kingdom Department of Electrical and Computer Engineering National Chiao Tung University Taiwan

A Bayesian approach termed the BAyesian Least Squares Optimization with Nonnegative L 1 -norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichlet distribution. With the Bayes rule, searching for the optimal parameters is equivalent to finding the mode of the posterior distribution. In order to explicitly characterize the nonnegative L 1 -norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution. We estimate the moments of the approximated Dirichlet posterior distribution by sampling methods. Four sampling methods have been introduced and implemented. With the estimated posterior distributions, the original parameters can be effectively reconstructed in polynomial fitting problems, and the BALSON framework is found to perform better than conventional methods.

关键词： Bayes methods Optimization Monte Carlo methods Sampling methods Linear programming Laplace equations Data models

New Big Data Collecting Method Based on Compressive Sensing in WSN

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New Big Data Collecting Method Based on Compressive Sensing ...

International Conference on Computer Communications and Networks (ICCCN)

作者： De-gan Zhang Xiao-hua Liu Yu-ya Cui Hong-tao Peng School of Computer Science &Engineering Tianjin University of Technology Tianjin China Computing & Novel software Technology Tianjin Key Lab of Intelligent Tianjin China Ministry of Education Key Laboratory of Computer Vision and System(TJUT) Tianjin China National Petroleum Corporation (CNPC) Managers Training Institute Beijing China

Considered the wireless sensor network clustering structure, a new big data collecting method based on compressive sensing is proposed. The collection process is as follows: in the cluster, the sink node sets the corresponding seed vector based on the distribution of network, and then sends it to each cluster head. Cluster head can generate corresponding own random spacing sparse matrix based on its received seed vector, and collect data through compressive sensing technology; Among clusters, clusters forward measurement values to sink node along multi-hop routing tree which we built before. Performance analyzing and comparison of results show that this method is superior to other methods regardless of in a cluster or inter-cluster.

关键词： Wireless sensor networks Compressed sensing Sparse matrices Big Data Routing Energy consumption Training

Modeling Multi-turn conversation with deep utterance aggregation

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arXiv 2018年

作者： Zhang, Zhuosheng Li, Jiangtong Zhu, Pengfei Zhao, Hai Liu, Gongshen Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 College of Zhiyuan Shanghai Jiao Tong University School of Cyber Security Shanghai Jiao Tong University School of Computer Science and Software Engineering East China Normal University

Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus. Copyright © 2018, The Authors. All rights reserved.

关键词： Speech processing

More than an answer: Neural pivot network for visual qestion answering 17

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More than an answer: Neural pivot network for visual qestion...

25th ACM International Conference on Multimedia, MM 2017

作者： Zhou, Yiyi Ji, Rongrong Su, Jinsong Wu, Yongjian Wu, Yunsheng Fujian Key Laboratory of Sensing and Computing for Smart City Xiamen University 361005 China School of Information Science and Engineering Xiamen University 361005 China School of Software Xiamen University 361005 China Tencent YouTu Lab. Tencent Technology Shanghai China

ISBN: (纸本)9781450349062

Most of existing works in visual question answering (VQA) are dedicated to improving the performance of answer predictions, while leaving the explanation of answering unexploited. We argue that, exploiting the explanations of question answering not only makes VQA explainable, but also quantitatively improves the prediction performance. In this paper, we propose a novel network architecture, termed Neural Pivot Network (NPN), towards simultaneous VQA and generating explanations in a multi-task learning architecture. NPN is trained by using both image-caption and image-question-answer pairs. In principle, CNN-based deep visual features are extracted and sent to both the VQA channel and the captioning module, the latter of which serves as a pivot to bridge the source image module to the target QA predictor. Such an innovative design enables us to introduce large-scale image-captioning training sets, e.g., MS-COCO Caption and Visual Genome Caption, together with cutting-edge image captioning models to benefit VQA learning. Quantitatively, the proposed NPN performs significantly better than alternatives and state-of-the-art schemes trained on VQA datasets only. Besides, by investigating the by-product of experiments, in-depth digests can be provided along with the answers. © 2017 ACM.

关键词： Network architecture