2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第9页

Sequence Generative Adversarial Network for Long Text Summarization 用于长文本摘要的序列生成对抗网络

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00045

Haoji Xu, Yanan Cao, Ruipeng Jia, Yanbing Liu, Jianlong Tan

{"title":"Sequence Generative Adversarial Network for Long Text Summarization","authors":"Haoji Xu, Yanan Cao, Ruipeng Jia, Yanbing Liu, Jianlong Tan","doi":"10.1109/ICTAI.2018.00045","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00045","url":null,"abstract":"In this paper, we propose a new adversarial training framework for text summarization task. Although sequence-to-sequence models have achieved state-of-the-art performance in abstractive summarization, the training strategy (MLE) suffers from exposure bias in the inference stage. This discrepancy between training and inference makes generated summaries less coherent and accuracy, which is more prominent in summarizing long articles. To address this issue, we model abstractive summarization using Generative Adversarial Network (GAN), aiming to minimize the gap between generated summaries and the ground-truth ones. This framework consists of two models: a generator that generates summaries, a discriminator that evaluates generated summaries. Reinforcement learning (RL) strategy is used to guarantee the co-training of generator and discriminator. Besides, motivated by the nature of summarization task, we design a novel Triple-RNNs discriminator, and extend the off-the-shelf generator by appending encoder and decoder with attention mechanism. Experimental results showed that our model significantly outperforms the state-of-the-art models, especially on long text corpus.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116856684","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: 4

Improved Spoken Uyghur Segmentation for Neural Machine Translation 基于神经机器翻译的维吾尔语语音切分方法

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00018

Chenggang Mi, Yating Yang, Xi Zhou, Lei Wang, Tonghai Jiang

引用次数: 0

A Novel Automatic Context-Based Similarity Metric for Local Outlier Detection Tasks 局部离群点检测任务中一种新的基于上下文的自动相似度度量

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00152

Fan Meng, Yang Gao, Ruili Wang

{"title":"A Novel Automatic Context-Based Similarity Metric for Local Outlier Detection Tasks","authors":"Fan Meng, Yang Gao, Ruili Wang","doi":"10.1109/ICTAI.2018.00152","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00152","url":null,"abstract":"Local outlier detection is able to capture local behavior to improve detection performance compared to traditional global outlier detection techniques. Most existing local outlier detection methods have the fundamental assumption that attributes and attribute values are independent and identically distributed (IID). However, in many situations, since the attributes usually have an inner structure, they should not be handled equally. To address the issue above, we propose a novel automatic context-based similarity metric for local outlier detection tasks. This paper mainly includes three aspects: (i) to propose a novel approach to automatically detect the contextual attributes by capturing the attribute intra-coupling and inter-coupling; (ii) to introduce a Non-IID similarity metric to derive the kNN set and reachability distance of an object based on the attribute structure and incorporate it into local outlier detection tasks; (iii) to build a data set called EG-Permission, which is a real-world data set from an E-Government Information System for context-based local outlier detection. Results obtained from 10 data sets show the proposed approach can identify the attribute structure effectively and improve the performance in local outlier detection tasks.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117151791","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

Weight Adjusted Naive Bayes 权重调整朴素贝叶斯

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00129

Liangjun Yu, Liangxiao Jiang, Lungan Zhang, Dianhong Wang

{"title":"Weight Adjusted Naive Bayes","authors":"Liangjun Yu, Liangxiao Jiang, Lungan Zhang, Dianhong Wang","doi":"10.1109/ICTAI.2018.00129","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00129","url":null,"abstract":"Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its simplicity, efficiency and efficacy, but the assumption of independence for attributes in NB is rarely true in reality. Attribute weighting is effective for overcoming the unrealistic assumption in NB, but it has received less attention than it warrants. Attribute weighting approaches can be broadly divided into two categories: filters and wrappers. In this paper, we mainly focus on wrapper attribute weighting approaches because they have generally higher classification performance than filter attribute weighting approaches. We propose a weight adjusted naive Bayes approach and simply denote it WANB. In WANB, the importance of each attribute in the classification of a training data set is learned and the weight vector reflecting this importance is updated. We use weight adjustment based on objective functions to find the optimal weight vector. We compare WANB with standard NB and its state-of-the-art attribute weighting approaches. Empirical studies on a collection of 36 benchmark datasets show that the classification performance of WANB significantly outperforms NB and all the existing filter approaches used to compare. Yet at the same time, compared to the existing wrapper approach called DEWANB, WANB is much more efficient and comprehensible.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124981751","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

Fine-Grained Hierarchical Classification of Plant Leaf Images Using Fusion of Deep Models 基于深度模型融合的植物叶片图像细粒度分层分类

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00011

Voncarlos M. Araújo, A. Britto, André L. Brun, Alessandro Lameiras Koerich, Luiz Oliveira

引用次数: 10

HetEOTL: An Algorithm for Heterogeneous Online Transfer Learning 异构在线迁移学习的一种算法

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00062

Qian Chen, Yunshu Du, Ming Xu, Chongjun Wang

引用次数: 4

Constrainedness in Stable Matching 稳定匹配中的约束

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00112

Guillaume Escamocher, B. O’Sullivan

引用次数: 0

Recursive Structure Similarity: A Novel Algorithm for Graph Clustering 递归结构相似度:一种新的图聚类算法

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00068

Yixin Fang, R. Jin, Wei Xiong, Xiaoning Qian, D. Dou, HaiNhat Phan

引用次数: 0

GT-Net: A Deep Learning Network for Gastric Tumor Diagnosis GT-Net:用于胃肿瘤诊断的深度学习网络

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00014

Yuexiang Li, Xinpeng Xie, Shaoxiong Liu, Xuechen Li, L. Shen

引用次数: 12

Inducing Readable Oblique Decision Trees 诱导可读的倾斜决策树

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2018-11-01 DOI: 10.1109/ICTAI.2018.00069

Antonin Leroux, M. Boussard, R. Dès

引用次数: 1