Journal of Big Data最新文献

{"title":"The role of classifiers and data complexity in learned Bloom filters: insights and recommendations","authors":"Dario Malchiodi, Davide Raimondi, Giacomo Fumagalli, Raffaele Giancarlo, Marco Frasca","doi":"10.1186/s40537-024-00906-9","DOIUrl":"https://doi.org/10.1186/s40537-024-00906-9","url":null,"abstract":"<p>Bloom filters, since their introduction over 50 years ago, have become a pillar to handle membership queries in small space, with relevant application in Big Data Mining and Stream Processing. Further improvements have been recently proposed with the use of Machine Learning techniques: learned Bloom filters. Those latter make considerably more complicated the proper parameter setting of this multi-criteria data structure, in particular in regard to the choice of one of its key components (the classifier) and accounting for the classification complexity of the input dataset. Given this State of the Art, our contributions are as follows. (1) A novel methodology, supported by software, for designing, analyzing and implementing learned Bloom filters that account for their own multi-criteria nature, in particular concerning classifier type choice and data classification complexity. Extensive experiments show the validity of the proposed methodology and, being our software public, we offer a valid tool to the practitioners interested in using learned Bloom filters. (2) Further contributions to the advancement of the State of the Art that are of great practical relevance are the following: (a) the classifier inference time should not be taken as a proxy for the filter reject time; (b) of the many classifiers we have considered, only two offer good performance; this result is in agreement with and further strengthens early findings in the literature; (c) Sandwiched Bloom filter, which is already known as being one of the references of this area, is further shown here to have the remarkable property of robustness to data complexity and classifier performance variability.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"5 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods","authors":"Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar","doi":"10.1186/s40537-024-00905-w","DOIUrl":"https://doi.org/10.1186/s40537-024-00905-w","url":null,"abstract":"<p>In the context of high-dimensional credit card fraud data, researchers and practitioners commonly utilize feature selection techniques to enhance the performance of fraud detection models. This study presents a comparison in model performance using the most important features selected by SHAP (SHapley Additive exPlanations) values and the model’s built-in feature importance list. Both methods rank features and choose the most significant ones for model assessment. To evaluate the effectiveness of these feature selection techniques, classification models are built using five classifiers: XGBoost, Decision Tree, CatBoost, Extremely Randomized Trees, and Random Forest. The Area under the Precision-Recall Curve (AUPRC) serves as the evaluation metric. All experiments are executed on the Kaggle Credit Card Fraud Detection Dataset. The experimental outcomes and statistical tests indicate that feature selection methods based on importance values outperform those based on SHAP values across classifiers and various feature subset sizes. For models trained on larger datasets, it is recommended to use the model’s built-in feature importance list as the primary feature selection method over SHAP. This suggestion is based on the rationale that computing SHAP feature importance is a distinct activity, while models naturally provide built-in feature importance as part of the training process, requiring no additional effort. Consequently, opting for the model’s built-in feature importance list can offer a more efficient and practical approach for larger datasets and more intricate models.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"6 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140298523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Multi-sample $$zeta $$ -mixup: richer, more realistic synthetic samples from a p-series interpolant","authors":"","doi":"10.1186/s40537-024-00898-6","DOIUrl":"https://doi.org/10.1186/s40537-024-00898-6","url":null,"abstract":"<h3>Abstract</h3> <p>Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of label information. A popular recent method, <em>mixup</em>, uses convex combinations of pairs of original samples to generate new samples. However, as we show in our experiments, <em>mixup</em> can produce undesirable synthetic samples, where the data is sampled off the manifold and can contain incorrect labels. We propose <span> <span>(zeta )</span> </span>-<em>mixup</em>, a generalization of <em>mixup</em> with provably and demonstrably desirable properties that allows convex combinations of <span> <span>({T} ge 2)</span> </span> samples, leading to more realistic and diverse outputs that incorporate information from <span> <span>({T})</span> </span> original samples by using a <em>p</em>-series interpolant. We show that, compared to <em>mixup</em>, <span> <span>(zeta )</span> </span>-<em>mixup</em> better preserves the intrinsic dimensionality of the original datasets, which is a desirable property for training generalizable models. Furthermore, we show that our implementation of <span> <span>(zeta )</span> </span>-<em>mixup</em> is faster than <em>mixup</em>, and extensive evaluation on controlled synthetic and 26 diverse real-world natural and medical image classification datasets shows that <span> <span>(zeta )</span> </span>-<em>mixup</em> outperforms <em>mixup</em>, CutMix, and traditional data augmentation techniques. The code will be released at https://github.com/kakumarabhishek/zeta-mixup.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"9 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Learning manifolds from non-stationary streams","authors":"Suchismit Mahapatra, Varun Chandola","doi":"10.1186/s40537-023-00872-8","DOIUrl":"https://doi.org/10.1186/s40537-023-00872-8","url":null,"abstract":"<p>Streaming adaptations of manifold learning based dimensionality reduction methods, such as <i>Isomap</i>, are based on the assumption that a small initial batch of observations is enough for exact learning of the manifold, while remaining streaming data instances can be cheaply mapped to this manifold. However, there are no theoretical results to show that this core assumption is valid. Moreover, such methods typically assume that the underlying data distribution is stationary and are not equipped to detect, or handle, sudden changes or gradual drifts in the distribution that may occur when the data is streaming. We present theoretical results to show that the quality of a manifold asymptotically converges as the size of data increases. We then show that a Gaussian Process Regression (GPR) model, that uses a manifold-specific kernel function and is trained on an initial batch of sufficient size, can closely approximate the state-of-art streaming Isomap algorithms, and the predictive variance obtained from the GPR prediction can be employed as an effective detector of changes in the underlying data distribution. Results on several synthetic and real data sets show that the resulting algorithm can effectively learn lower dimensional representation of high dimensional data in a streaming setting, while identifying shifts in the generative distribution. For instance, key findings on a Gas sensor array data set show that our method can detect changes in the underlying data stream, triggered due to real-world factors, such as introduction of a new gas in the system, while efficiently mapping data on a low-dimensional manifold.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"26 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"An adaptive hybrid african vultures-aquila optimizer with Xgb-Tree algorithm for fake news detection","authors":"Amr A. Abd El-Mageed, Amr A. Abohany, Asmaa H. Ali, Khalid M. Hosny","doi":"10.1186/s40537-024-00895-9","DOIUrl":"https://doi.org/10.1186/s40537-024-00895-9","url":null,"abstract":"<p>Online platforms and social networking have increased in the contemporary years. They are now a major news source worldwide, leading to the online proliferation of Fake News (FNs). These FNs are alarming because they fundamentally reshape public opinion, which may cause customers to leave these online platforms, threatening the reputations of several organizations and industries. This rapid dissemination of FNs makes it imperative for automated systems to detect them, encouraging many researchers to propose various systems to classify news articles and detect FNs automatically. In this paper, a Fake News Detection (FND) methodology is presented based on an effective IBAVO-AO algorithm, which stands for hybridization of African Vultures Optimization (AVO) and Aquila Optimization (AO) algorithms, with an extreme gradient boosting Tree (Xgb-Tree) classifier. The suggested methodology involves three main phases: Initially, the unstructured FNs dataset is analyzed, and the essential features are extracted by tokenizing, encoding, and padding the input news words into a sequence of integers utilizing the GLOVE approach. Then, the extracted features are filtered using the effective Relief algorithm to select only the appropriate ones. Finally, the recovered features are used to classify the news items using the suggested IBAVO-AO algorithm based on the Xgb-Tree classifier. Hence, the suggested methodology is distinguished from prior models in that it performs automatic data pre-processing, optimization, and classification tasks. The proposed methodology is carried out on the ISOT-FNs dataset, containing more than 44 thousand multiple news articles divided into truthful and fake. We validated the proposed methodology’s reliability by examining numerous evaluation metrics involving accuracy, fitness values, the number of selected features, Kappa, Precision, Recall, F1-score, Specificity, Sensitivity, ROC_AUC, and MCC. Then, the proposed methodology is compared against the most common meta-heuristic optimization algorithms utilizing the ISOT-FNs. The experimental results reveal that the suggested methodology achieved optimal classification accuracy and F1-score and successfully categorized more than 92.5% of news articles compared to its peers. This study will assist researchers in expanding their understanding of meta-heuristic optimization algorithms applications for FND.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"38 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140168840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Integration of transcriptomic analysis and multiple machine learning approaches identifies NAFLD progression-specific hub genes to reveal distinct genomic patterns and actionable targets","authors":"Jing Sun, Run Shi, Yang Wu, Yan Lou, Lijuan Nie, Chun Zhang, Yutian Cao, Qianhua Yan, Lifang Ye, Shu Zhang, Xuanbin Wang, Qibiao Wu, Xuehua Jiao, Jiangyi Yu, Zhuyuan Fang, Xiqiao Zhou","doi":"10.1186/s40537-024-00899-5","DOIUrl":"https://doi.org/10.1186/s40537-024-00899-5","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Nonalcoholic fatty liver disease (NAFLD) is a leading public health problem worldwide. Approximately one fourth of patients with nonalcoholic fatty liver (NAFL) progress to nonalcoholic steatohepatitis (NASH), an advanced stage of NAFLD. Hence, there is an urgent need to make a better understanding of NAFLD heterogeneity and facilitate personalized management of high-risk NAFLD patients who may benefit from more intensive surveillance and preventive intervene.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>In this study, a series of bioinformatic methods were performed to identify NAFLD progression-specific pathways and genes, and three machine learning approaches were combined to construct a risk-stratification gene signature to quantify risk assessment. In addition, bulk RNA-seq, single-cell RNA-seq (scRNA-seq) transcriptome profiling data and whole-exome sequencing (WES) data were comprehensively analyzed to reveal the genomic alterations and altered pathways between distinct molecular subtypes.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Two distinct subtypes of NAFL were identified with the NAFLD progression-specific genes, and one subtype has a high similarity of the inflammatory pattern and fibrotic potential with NASH. The established risk-stratification gene signature could discriminate advanced samples from overall NAFLD. COL1A2, one key gene closely related to NAFLD progression, is specifically expressed in fibroblasts involved in hepatocellular carcinoma (HCC), and significantly correlated with EMT and angiogenesis in pan-cancer. Moreover, the β-catenin/COL1A2 axis might play a critical role in fibrosis severity and inflammatory response during NAFLD-HCC progression.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>In summary, our study provided evidence for the necessity of molecular classification and established a risk-stratification gene signature to quantify risk assessment of NAFLD, aiming to identify different risk subsets and to guide personalized treatment.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"2 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140154958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Where you go is who you are: a study on machine learning based semantic privacy attacks","authors":"Nina Wiedemann, Krzysztof Janowicz, Martin Raubal, Ourania Kounadi","doi":"10.1186/s40537-024-00888-8","DOIUrl":"https://doi.org/10.1186/s40537-024-00888-8","url":null,"abstract":"<p>Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200 m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"19 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140125539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 adaptive community-response (ACR) method for collecting misinformation on social media","authors":"Julian Kauk, Helene Kreysa, André Scherag, Stefan R. Schweinberger","doi":"10.1186/s40537-024-00894-w","DOIUrl":"https://doi.org/10.1186/s40537-024-00894-w","url":null,"abstract":"<p>Social media can be a major accelerator of the spread of misinformation, thereby potentially compromising both individual well-being and social cohesion. Despite significant recent advances, the study of online misinformation is a relatively young field facing several (methodological) challenges. In this regard, the detection of online misinformation has proven difficult, as online large-scale data streams require (semi-)automated, highly specific and therefore sophisticated methods to separate posts containing misinformation from irrelevant posts. In the present paper, we introduce the adaptive community-response (ACR) method, an unsupervised technique for the large-scale collection of misinformation on Twitter (now known as ’X’). The ACR method is based on previous findings showing that Twitter users occasionally reply to misinformation with fact-checking by referring to specific fact-checking sites (crowdsourced fact-checking). In a first step, we captured such misinforming but fact-checked tweets. These tweets were used in a second step to extract specific linguistic features (keywords), enabling us to collect also those misinforming tweets that were not fact-checked at all as a third step. We initially present a mathematical framework of our method, followed by an explicit algorithmic implementation. We then evaluate ACR on the basis of a comprehensive dataset consisting of <span>(&gt;25)</span> million tweets, belonging to <span>(&gt;300)</span> misinforming stories. Our evaluation shows that ACR is a useful extension to the methods pool of the field, enabling researchers to collect online misinformation more comprehensively. Text similarity measures clearly indicated correspondence between the claims of false stories and the ACR tweets, even though ACR performance was heterogeneously distributed across the stories. A baseline comparison to the fact-checked tweets showed that the ACR method can detect story-related tweets to a comparable degree, while being sensitive to different types of tweets: Fact-checked tweets tend to be driven by high outreach (as indicated by a high number of retweets), whereas the sensitivity of the ACR method extends to tweets exhibiting lower outreach. Taken together, ACR’s capacity as a valuable methodological contribution to the field is based on (i) the adoption of prior, pioneering research in the field, (ii) a well-formalized mathematical framework and (iii) an empirical foundation via a comprehensive set of indicators.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"130 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139968856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning","authors":"Jing Li, Mohd Shahizan Othman, Hewan Chen, Lizawati Mi Yusuf","doi":"10.1186/s40537-024-00892-y","DOIUrl":"https://doi.org/10.1186/s40537-024-00892-y","url":null,"abstract":"<p>Internet of Things (IoT) devices are widely used but also vulnerable to cyberattacks that can cause security issues. To protect against this, machine learning approaches have been developed for network intrusion detection in IoT. These often use feature reduction techniques like feature selection or extraction before feeding data to models. This helps make detection efficient for real-time needs. This paper thoroughly compares feature extraction and selection for IoT network intrusion detection in machine learning-based attack classification framework. It looks at performance metrics like accuracy, f1-score, and runtime, etc. on the heterogenous IoT dataset named Network TON-IoT using binary and multiclass classification. Overall, feature extraction gives better detection performance than feature selection as the number of features is small. Moreover, extraction shows less feature reduction compared with that of selection, and is less sensitive to changes in the number of features. However, feature selection achieves less model training and inference time compared with its counterpart. Also, more space to improve the accuracy for selection than extraction when the number of features changes. This holds for both binary and multiclass classification. The study provides guidelines for selecting appropriate intrusion detection methods for particular scenarios. Before, the TON-IoT heterogeneous IoT dataset comparison and recommendations were overlooked. Overall, the research presents a thorough comparison of feature reduction techniques for machine learning-driven intrusion detection in IoT networks.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"49 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139952919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Measuring regularity of human physical activities with entropy models","authors":"Keqin Shi, Zhen Chen, Weiqiang Sun, Weisheng Hu","doi":"10.1186/s40537-024-00891-z","DOIUrl":"https://doi.org/10.1186/s40537-024-00891-z","url":null,"abstract":"<p>Regularity is an important aspect of physical activity that can provide valuable insights into how individuals engage in physical activity over time. Accurate measurement of regularity not only advances our understanding of physical activity behavior but also facilitates the development of human activity modeling and forecasting. Furthermore, it can inform the design and implementation of tailored interventions to improve population health outcomes. In this paper, we aim to assess the regularity of physical activities through longitudinal sensor data, which reflects individuals’ all physical activities over an extended period. We explore three entropy models, including entropy rate, approximate entropy, and sample entropy, which can potentially offer a more comprehensive evaluation of physical activity regularity compared to metrics based solely on periodicity or stability. We propose a framework to validate the performance of entropy models on both synthesized and real-world physical activity data. The results indicate entropy rate is able to identify not only the magnitude and amount of noise but also macroscopic variations of physical activities, such as differences on duration and occurrence time. Simultaneously, entropy rate is highly correlated with the predictability of real-world samples, further highlighting its applicability in measuring human physical activity regularity. Leveraging entropy rate, we further investigate the regularity for 686 individuals. We find the composition of physical activities can partially explain the difference in regularity among individuals, and the majority of individuals exhibit temporal stability of regularity.</p>","PeriodicalId":15158,"journal":{"name":"Journal of Big Data","volume":"60 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139952796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}