Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00045
Haoji Xu, Yanan Cao, Ruipeng Jia, Yanbing Liu, Jianlong Tan
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.
{"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}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00018
Chenggang Mi, Yating Yang, Xi Zhou, Lei Wang, Tonghai Jiang
To increase vocabulary overlap in spoken Uyghur neural machine translation (NMT), we propose a novel method to enhance the common used subword units based segmentation method. In particular, we apply a log-linear model as the main framework and integrate several features such as subword, morphological information, bilingual word alignment and monolingual language model into it. Experimental results show that spoken Uyghur segmentation with our proposed method improves the performance of the spoken Uyghur-Chinese NMT significantly (yield up to 1.52 BLEU improvements).
{"title":"Improved Spoken Uyghur Segmentation for Neural Machine Translation","authors":"Chenggang Mi, Yating Yang, Xi Zhou, Lei Wang, Tonghai Jiang","doi":"10.1109/ICTAI.2018.00018","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00018","url":null,"abstract":"To increase vocabulary overlap in spoken Uyghur neural machine translation (NMT), we propose a novel method to enhance the common used subword units based segmentation method. In particular, we apply a log-linear model as the main framework and integrate several features such as subword, morphological information, bilingual word alignment and monolingual language model into it. Experimental results show that spoken Uyghur segmentation with our proposed method improves the performance of the spoken Uyghur-Chinese NMT significantly (yield up to 1.52 BLEU improvements).","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"18 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":"124300323","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}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00152
Fan Meng, Yang Gao, Ruili Wang
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.
{"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}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00129
Liangjun Yu, Liangxiao Jiang, Lungan Zhang, Dianhong Wang
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.
{"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}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00011
Voncarlos M. Araújo, A. Britto, André L. Brun, Alessandro Lameiras Koerich, Luiz Oliveira
A fine-grained plant leaf classification method based on the fusion of deep models is described. Complementary global and patch-based leaf features are combined at each hierarchical level (genus and species) by pre-trained CNNs. The deep models are adapted for plant recognition by using data augmentation techniques to face the problem of plant classes with very few samples for training in the available imbalanced dataset. Experimental results have shown that the proposed coarse-to-fine classification strategy is a very promising alternative to deal with the low inter-class and high intra-class variability inherent to the problem of plant identification. The proposed method was able to surpass other state-of-the-art approaches on the ImageCLEF 2015 plant recognition dataset in terms of average classification scores.
{"title":"Fine-Grained Hierarchical Classification of Plant Leaf Images Using Fusion of Deep Models","authors":"Voncarlos M. Araújo, A. Britto, André L. Brun, Alessandro Lameiras Koerich, Luiz Oliveira","doi":"10.1109/ICTAI.2018.00011","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00011","url":null,"abstract":"A fine-grained plant leaf classification method based on the fusion of deep models is described. Complementary global and patch-based leaf features are combined at each hierarchical level (genus and species) by pre-trained CNNs. The deep models are adapted for plant recognition by using data augmentation techniques to face the problem of plant classes with very few samples for training in the available imbalanced dataset. Experimental results have shown that the proposed coarse-to-fine classification strategy is a very promising alternative to deal with the low inter-class and high intra-class variability inherent to the problem of plant identification. The proposed method was able to surpass other state-of-the-art approaches on the ImageCLEF 2015 plant recognition dataset in terms of average classification scores.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"56 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":"123440580","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}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00062
Qian Chen, Yunshu Du, Ming Xu, Chongjun Wang
Transfer learning is an important topic in machine learning and has been broadly studied for many years. However, most existing transfer learning methods assume the training sets are prepared in advance, which is often not the case in practice. Fortunately, online transfer learning (OTL), which addresses the transfer learning tasks in an online fashion, has been proposed to solve the problem. This paper mainly focuses on the heterogeneous OTL, which is in general very challenging because the feature space of target domain is different from that of the source domain. In order to enhance the learning performance, we designed the algorithm called Heterogeneous Ensembled Online Transfer Learning (HetEOTL) using ensemble learning strategy. Finally, we evaluate our algorithm on some benchmark datasets, and the experimental results show that HetEOTL has better performance than some other existing online learning and transfer learning algorithms, which proves the effectiveness of HetEOTL.
迁移学习是机器学习中的一个重要课题，已被广泛研究多年。然而，大多数现有的迁移学习方法都假设训练集是预先准备好的，而在实践中往往不是这样。幸运的是，在线迁移学习(online transfer learning, OTL)已经被提出来解决这个问题，它以在线的方式处理迁移学习任务。本文主要研究的是异构OTL，由于目标域的特征空间与源域的特征空间不同，异构OTL具有很大的挑战性。为了提高学习性能，采用集成学习策略设计了异构集成在线迁移学习算法(HetEOTL)。最后，我们在一些基准数据集上对算法进行了评估，实验结果表明，HetEOTL比现有的一些在线学习和迁移学习算法具有更好的性能，证明了HetEOTL的有效性。
{"title":"HetEOTL: An Algorithm for Heterogeneous Online Transfer Learning","authors":"Qian Chen, Yunshu Du, Ming Xu, Chongjun Wang","doi":"10.1109/ICTAI.2018.00062","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00062","url":null,"abstract":"Transfer learning is an important topic in machine learning and has been broadly studied for many years. However, most existing transfer learning methods assume the training sets are prepared in advance, which is often not the case in practice. Fortunately, online transfer learning (OTL), which addresses the transfer learning tasks in an online fashion, has been proposed to solve the problem. This paper mainly focuses on the heterogeneous OTL, which is in general very challenging because the feature space of target domain is different from that of the source domain. In order to enhance the learning performance, we designed the algorithm called Heterogeneous Ensembled Online Transfer Learning (HetEOTL) using ensemble learning strategy. Finally, we evaluate our algorithm on some benchmark datasets, and the experimental results show that HetEOTL has better performance than some other existing online learning and transfer learning algorithms, which proves the effectiveness of HetEOTL.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"95 1 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":"128685053","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}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00112
Guillaume Escamocher, B. O’Sullivan
In constraint satisfaction problems, constrainedness provides a way to predict the number of solutions: for instances of a same size, the number of constraints is inversely correlated with the number of solutions. However, there is no obvious equivalent metric for stable matching problems. We introduce the contrarian score, a simple metric that is to matching problems what constrainedness is to constraint satisfaction problems. In addition to comparing the contrarian score against other potential tightness metrics, we test it for different instance sizes as well as extremely distinct versions of the stable matching problem. In all cases, we find that the correlation between contrarian score and number of solutions is very strong.
{"title":"Constrainedness in Stable Matching","authors":"Guillaume Escamocher, B. O’Sullivan","doi":"10.1109/ICTAI.2018.00112","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00112","url":null,"abstract":"In constraint satisfaction problems, constrainedness provides a way to predict the number of solutions: for instances of a same size, the number of constraints is inversely correlated with the number of solutions. However, there is no obvious equivalent metric for stable matching problems. We introduce the contrarian score, a simple metric that is to matching problems what constrainedness is to constraint satisfaction problems. In addition to comparing the contrarian score against other potential tightness metrics, we test it for different instance sizes as well as extremely distinct versions of the stable matching problem. In all cases, we find that the correlation between contrarian score and number of solutions is very strong.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"44 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":"128939655","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}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00068
Yixin Fang, R. Jin, Wei Xiong, Xiaoning Qian, D. Dou, HaiNhat Phan
A various number of graph clustering algorithms have been proposed and applied in real-world applications such as network analysis, bio-informatics, social computing, and etc. However, existing algorithms usually focus on optimizing specified quality measures at the global network level, without carefully considering the destruction of local structures which could be informative and significant in practice. In this paper, we propose a novel clustering algorithm for undirected graphs based on a new structure similarity measure which is computed in a recursive procedure. Our method can provide robust and high-quality clustering results, while preserving informative local structures in the original graph. Rigorous experiments conducted on a variety of benchmark and protein datasets show that our algorithm consistently outperforms existing algorithms.
{"title":"Recursive Structure Similarity: A Novel Algorithm for Graph Clustering","authors":"Yixin Fang, R. Jin, Wei Xiong, Xiaoning Qian, D. Dou, HaiNhat Phan","doi":"10.1109/ICTAI.2018.00068","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00068","url":null,"abstract":"A various number of graph clustering algorithms have been proposed and applied in real-world applications such as network analysis, bio-informatics, social computing, and etc. However, existing algorithms usually focus on optimizing specified quality measures at the global network level, without carefully considering the destruction of local structures which could be informative and significant in practice. In this paper, we propose a novel clustering algorithm for undirected graphs based on a new structure similarity measure which is computed in a recursive procedure. Our method can provide robust and high-quality clustering results, while preserving informative local structures in the original graph. Rigorous experiments conducted on a variety of benchmark and protein datasets show that our algorithm consistently outperforms existing algorithms.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"72 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":"124184906","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}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00014
Yuexiang Li, Xinpeng Xie, Shaoxiong Liu, Xuechen Li, L. Shen
Gastric cancer is one of the most common cancers, which causes the second largest number of deaths in the world. Traditional diagnosis approach requires pathologists to manually annotate the gastric tumor in gastric slice for cancer identification, which is laborious and time-consuming. In this paper, we proposed a deep learning based framework, namely GT-Net, for automatic segmentation of gastric tumor. The proposed GT-Net adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our GT-Net performs better than state-of-the-art networks like FCN-8s, U-net, and achieved a new state-of-the-art F1 score of 90.88% for gastric tumor segmentation.
{"title":"GT-Net: A Deep Learning Network for Gastric Tumor Diagnosis","authors":"Yuexiang Li, Xinpeng Xie, Shaoxiong Liu, Xuechen Li, L. Shen","doi":"10.1109/ICTAI.2018.00014","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00014","url":null,"abstract":"Gastric cancer is one of the most common cancers, which causes the second largest number of deaths in the world. Traditional diagnosis approach requires pathologists to manually annotate the gastric tumor in gastric slice for cancer identification, which is laborious and time-consuming. In this paper, we proposed a deep learning based framework, namely GT-Net, for automatic segmentation of gastric tumor. The proposed GT-Net adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our GT-Net performs better than state-of-the-art networks like FCN-8s, U-net, and achieved a new state-of-the-art F1 score of 90.88% for gastric tumor segmentation.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 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":"126907954","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}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00069
Antonin Leroux, M. Boussard, R. Dès
Although machine learning models are found in more and more practical applications, stakeholders can be suspicious about the fact that they are not hard-coded and fully specified. To foster trust, it is crucial to provide models whose predictions are explainable. Decision Trees can be understood by humans if they are simple enough, but they suffer in accuracy when compared to other common machine learning methods. Oblique Decision Trees can provide better accuracy and smaller trees, but their decision rules are more complex. This article presents MUST (Multivariate Understandable Statistical Tree), an Oblique Decision Tree split algorithm based on Linear Discriminant Analysis that aims to preserve explainability by limiting the number of variables that appear in decision rules.
{"title":"Inducing Readable Oblique Decision Trees","authors":"Antonin Leroux, M. Boussard, R. Dès","doi":"10.1109/ICTAI.2018.00069","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00069","url":null,"abstract":"Although machine learning models are found in more and more practical applications, stakeholders can be suspicious about the fact that they are not hard-coded and fully specified. To foster trust, it is crucial to provide models whose predictions are explainable. Decision Trees can be understood by humans if they are simple enough, but they suffer in accuracy when compared to other common machine learning methods. Oblique Decision Trees can provide better accuracy and smaller trees, but their decision rules are more complex. This article presents MUST (Multivariate Understandable Statistical Tree), an Oblique Decision Tree split algorithm based on Linear Discriminant Analysis that aims to preserve explainability by limiting the number of variables that appear in decision rules.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"25 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":"127245462","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}
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