Intelligent Data Analysis最新文献

{"title":"ELCA: Enhanced boundary location for Chinese named entity recognition via contextual association","authors":"Yizhao Wang, Shun Mao, Yuncheng Jiang","doi":"10.3233/ida-230383","DOIUrl":"https://doi.org/10.3233/ida-230383","url":null,"abstract":"Named Entity Recognition (NER) is a fundamental task that aids in the completion of other tasks such as text understanding, information retrieval and question answering in Natural Language Processing (NLP). In recent years, the use of a mix of character-word structure and dictionary information forChinese NER has been demonstrated to be effective. As a representative of hybrid models, Lattice-LSTM has obtained better benchmarking results in several publicly available Chinese NER datasets. However, Lattice-LSTM does not address the issue of long-distance entities or the detection of several entities with the same character. At the same time, the ambiguity of entity boundary information also leads to a decrease in the accuracy of embedding NER. This paper proposes ELCA: Enhanced Boundary Location for Chinese Named Entity Recognition Via Contextual Association, a method that solves the problem of long-distance dependent entities by using sentence-level position information. At the same time, it uses adaptive word convolution to overcome the problem of several entities sharing the same character. ELCA achieves the state-of-the-art outcomes in Chinese Word Segmentation and Chinese NER.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"23 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying relevant features of CSE-CIC-IDS2018 dataset for the development of an intrusion detection system","authors":"László Göcs, Zsolt Csaba Johanyák","doi":"10.3233/ida-230264","DOIUrl":"https://doi.org/10.3233/ida-230264","url":null,"abstract":"Intrusion detection systems (IDSs) are essential elements of IT systems. Their key component is a classification module that continuously evaluates some features of the network traffic and identifies possible threats. Its efficiency is greatly affected by the right selection of the features to be monitored. Therefore, the identification of a minimal set of features that are necessary to safely distinguish malicious traffic from benign traffic is indispensable in the course of the development of an IDS. This paper presents the preprocessing and feature selection workflow as well as its results in the case of the CSE-CIC-IDS2018 on AWS dataset, focusing on five attack types. To identify the relevant features, six feature selection methods were applied, and the final ranking of the features was elaborated based on their average score. Next, several subsets of the features were formed based on different ranking threshold values, and each subset was tried with five classification algorithms to determine the optimal feature set for each attack type. During the evaluation, four widely used metrics were taken into consideration.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"2017 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge graph embedding in a uniform space","authors":"Da Tong, Shudong Chen, Rong Ma, Donglin Qi, Yong Yu","doi":"10.3233/ida-227123","DOIUrl":"https://doi.org/10.3233/ida-227123","url":null,"abstract":"Knowledge graph embedding (KGE) is typically used for link prediction to automatically predict missing links in knowledge graphs. Current KGE models are mainly based on complicated mathematical associations, which are highly expressive but ignore the uniformity behind the classical bilinear translational model TransE, a model that embeds all entities of knowledge graphs in a uniform space, enabling accurate embeddings. This study analyses the uniformity of TransE and proposes a novel KGE model called ConvUs that follows uniformity with expressiveness. Based on the convolution neural network (CNN), ConvUs proposes constraints on convolution filter values and employs a multi-layer, multi-scale CNN architecture with a non-parametric L2 norm-based scoring function for the calculation of triple scores. This addresses potential uniformity-related issues in existing CNN-based KGE models, allowing ConvUs to maintain a uniform embedding space while benefiting from the powerful expressiveness of CNNs. Furthermore, circular convolution is applied to alleviate the potential orderliness contradictions, making ConvUs more suitable for conducting uniform space KGE. Our model outperformed the base model ConvKB and several baselines on the link prediction benchmark WN18RR and FB15k-237, demonstrating strong applicability and generalization and indicating that the uniformity of embedding space with high expressiveness enables more efficient knowledge graph embeddings.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"5 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139927130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of consultative leadership on administrative development","authors":"Ayas Mohammed Rasheed Omar, Khairi Ali Auso","doi":"10.3233/ida-237448","DOIUrl":"https://doi.org/10.3233/ida-237448","url":null,"abstract":"Consultative leadership is a democratic style that deliberately incorporates employees into organizational management and decision-making to increase employees’ feelings of ownership and align their objectives with company objectives. As a result, during their everyday tasks, leaders constantly utilize “consultation management” for their staff. As examples, consider how to coordinate reports, communicate key ideas, and use a variety of flexible promotion strategies. This study investigates the role of consultive leadership on administrative development in developing countries. For this reason, this study has applied a questionnaire to take the respondents’ opinions in the Iraqi ministry of interior affairs. Using the Likert scale has provided quantitative value for the qualitative study. For this reason, questionnaires were provided, and this study’s results showed a positive correlation between consultative leadership and administrative development. As a result, the organization’s leader has more chances to administer the organization successfully than a manager or an unofficial leader who lacks status power.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139201563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning based software effort estimation using development-centric features for crowdsourcing platform","authors":"Anum Yasmin, Wasi Haider, Ali Daud, Ameena T Banjar","doi":"10.3233/ida-237366","DOIUrl":"https://doi.org/10.3233/ida-237366","url":null,"abstract":"Crowd-Sourced software development (CSSD) is getting a good deal of attention from the software and research community in recent times. One of the key challenges faced by CSSD platforms is the task selection mechanism which in practice, contains no intelligent scheme. Rather, rule-of-thumb or intuition strategies are employed, leading to biasness and subjectivity. Effort considerations on crowdsourced tasks can offer good foundation for task selection criteria but are not much investigated. Software development effort estimation (SDEE) is quite prevalent domain in software engineering but only investigated for in-house development. For open-sourced or crowdsourced platforms, it is rarely explored. Moreover, Machine learning (ML) techniques are overpowering SDEE with a claim to provide more accurate estimation results. This work aims to conjoin ML-based SDEE to analyze development effort measures on CSSD platform. The purpose is to discover development-oriented features for crowdsourced tasks and analyze performance of ML techniques to find best estimation model on CSSD dataset. TopCoder is selected as target CSSD platform for the study. TopCoder’s development tasks data with development-centric features are extracted, leading to statistical, regression and correlation analysis to justify features’ significance. For effort estimation, 10 ML families with 2 respective techniques are applied to get broader aspect of estimation. Five performance metrices (MSE, RMSE, MMRE, MdMRE, Pred (25) and Welch’s statistical test are incorporated to judge the worth of effort estimation model’s performance. Data analysis results show that selected features of TopCoder pertain reasonable model significance, regression, and correlation measures. Findings of ML effort estimation depicted that best results for TopCoder dataset can be acquired by linear, non-linear regression and SVM family models. To conclude, the study identified the most relevant development features for CSSD platform, confirmed by in-depth data analysis. This reflects careful selection of effort estimation features to offer good basis of accurate ML estimate.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"29 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139201259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic and financial news hybrid- classification based on category-associated feature set","authors":"Wilawan Yathongkhum, Y. Laosiritaworn, Jakramate Bootkrajang, Pucktada Treeratpituk, Jeerayut Chaijaruwanich","doi":"10.3233/ida-237373","DOIUrl":"https://doi.org/10.3233/ida-237373","url":null,"abstract":"A large amount of economic and financial news is now accessible through various news websites and social media platforms. Categorizing them into appropriate categories can be advantageous for various tasks, such as sentiment analysis and news-based market prediction. Unfortunately, news headlines categories may contain ambiguities due to the subjective nature of label assignment by authors or publishers. Consequently, achieving precise classification of news can be time-consuming and still reliant on human expertise. To tackle this challenging task, we proposed a hybrid approach to enhance the performance of economic and financial news classification. This approach combines baseline classifiers with a novel method called the Category Associated Feature Set (CAFS) classifier. CAFS transforms text input from the lexicon-space into the entity-space and discovers associations between entities and classes, akin to association rule learning. Experimental results on three datasets demonstrated that the proposed method is comparable to existing approaches and exhibits a significant improvement in the classification results for out-of-domain datasets. Additionally, employing CAFS in tandem with the existing text classification baselines can provide a general categorizer for distinguishing news categories across various sources without the need for extensive fine-tuning of the parameters associated with those classification baselines. This confirms that utilizing CAFS in a hybrid approach is appropriate and suitable for economic and financial news classification.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"5 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139202492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feature-level mask self-supervised assisted learning approach based on transformer for remaining useful life prediction","authors":"Bing Xue, Xin Gao, Shuwei Zhang, Ning Wang, Shiyuan Fu, Jiahao Yu, Guangyao Zhang, Zijian Huang","doi":"10.3233/ida-227099","DOIUrl":"https://doi.org/10.3233/ida-227099","url":null,"abstract":"Nowadays, the massive industrial data has effectively improved the performance of the data-driven deep learning Remaining Useful Life (RUL) prediction method. However, there are still problems of assigning fixed weights to features and only coarse-grained consideration at the sequence level. This paper proposes a Transformer-based end-to-end feature-level mask self-supervised learning method for RUL prediction. First, by proposing a fine-grained feature-level mask self-supervised learning method, the data at different time points under all features in a time window is sent to two parallel learning streams with and without random masks. The model can learn more fine-grained degradation information by comparing the information extracted by the two parallel streams. Instead of assigning fixed weights to different features, the abstract information extracted through the above process is invariable correlations between features, which has a good generalization to various situations under different working conditions. Then, the extracted information is encoded and decoded again using an asymmetric structure, and a fully connected network is used to build a mapping between the extracted information and the RUL. We conduct experiments on the public C-MAPSS datasets and show that the proposed method outperforms the other methods, and its advantages are more obvious in complex multi-working conditions.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"17 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139200197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Adaboost performance in the presence of class-label noise: A comparative study on EEG-based classification of schizophrenic patients and benchmark datasets","authors":"O. R. Pouya, Reza Boostani, M. Sabeti","doi":"10.3233/ida-227125","DOIUrl":"https://doi.org/10.3233/ida-227125","url":null,"abstract":"The performance of Adaboost is highly sensitive to noisy and outlier samples. This is therefore the weights of these samples are exponentially increased in successive rounds. In this paper, three novel schemes are proposed to hunt the corrupted samples and eliminate them through the training process. The methods are: I) a hybrid method based on K-means clustering and K-nearest neighbor, II) a two-layer Adaboost, and III) soft margin support vector machines. All of these solutions are compared to the standard Adaboost on thirteen Gunnar Raetsch’s datasets under three levels of class-label noise. To test the proposed method on a real application, electroencephalography (EEG) signals of 20 schizophrenic patients and 20 age-matched control subjects, are recorded via 20 channels in the idle state. Several features including autoregressive coefficients, band power and fractal dimension are extracted from EEG signals of all participants. Sequential feature subset selection technique is adopted to select the discriminative EEG features. Experimental results imply that exploiting the proposed hunting techniques enhance the Adaboost performance as well as alleviating its robustness against unconfident and noisy samples over Raetsch benchmark and EEG features of the two groups.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"32 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139199232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building knowledge graphs from technical documents using named entity recognition and edge weight updating neural network with triplet loss for entity normalization","authors":"Sung Hwan Jeon, Hyeonguk Lee, Jihye Park, Sungzoon Cho","doi":"10.3233/ida-227129","DOIUrl":"https://doi.org/10.3233/ida-227129","url":null,"abstract":"Attempts to express information from various documents in graph form are rapidly increasing. The speed and volume in which these documents are being generated call for an automated process, based on machine learning techniques, for cost-effective and timely analysis. Past studies responded to such needs by building knowledge graphs or technology trees from the bibliographic information of documents, or by relying on text mining techniques in order to extract keywords and/or phrases. While these approaches provide an intuitive glance into the technological hotspots or the key features of the select field, there still is room for improvement, especially in terms of recognizing the same entities appearing in different forms so as to interconnect closely related technological concepts properly. In this paper, we propose to build a patent knowledge network using the United States Patent and Trademark Office (USPTO) patent filings for the semiconductor device sector by fine-tuning Huggingface’s named entity recognition (NER) model with our novel edge weight updating neural network. For the named entity normalization, we employ edge weight updating neural network with positive and negative candidates that are chosen by substring matching techniques. Experiment results show that our proposed approach performs very competitively against the conventional keyword extraction models frequently employed in patent analysis, especially for the named entity normalization (NEN) and document retrieval tasks. By grouping entities with named entity normalization model, the resulting knowledge graph achieves higher scores in retrieval tasks. We also show that our model is robust to the out-of-vocabulary problem by employing the fine-tuned BERT NER model.","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"173 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139208091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MeFiNet: Modeling multi-semantic convolution-based feature interactions for CTR prediction","authors":"Cairong Yan, Xiaoke Li, Ran Tao, Zhaohui Zhang, Yongquan Wan","doi":"10.3233/ida-227113","DOIUrl":"https://doi.org/10.3233/ida-227113","url":null,"abstract":"Extracting more information from feature interactions is essential to improve click-through rate (CTR) prediction accuracy. Although deep learning technology can help capture high-order feature interactions, the combination of features lacks interpretability. In this paper, we propose a multi-semantic feature interaction learning network (MeFiNet), which utilizes convolution operations to map feature interactions to multi-semantic spaces to improve their expressive ability and uses an improved Squeeze & Excitation method based on SENet to learn the importance of these interactions in different semantic spaces. The Squeeze operation helps to obtain the global importance distribution of semantic spaces, and the Excitation operation helps to dynamically re-assign the weights of semantic features so that both semantic diversity and feature diversity are considered in the model. The generated multi-semantic feature interactions are concatenated with the original feature embeddings and input into a deep learning network. Experiments on three public datasets demonstrate the effectiveness of the proposed model. Compared with state-of-the-art methods, the model achieves excellent performance (+0.18% in AUC and -0.34% in LogLoss VS DeepFM; +0.19% in AUC and -0.33% in LogLoss VS FiBiNet).","PeriodicalId":50355,"journal":{"name":"Intelligent Data Analysis","volume":"14 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139197290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}