Journal of Algorithms & Computational Technology最新文献

Inexact fixed-point iteration method for nonlinear complementarity problems 非线性互补问题的不精确不动点迭代法

Journal of Algorithms & Computational Technology Pub Date : 2023-01-01 DOI: 10.1177/17483026231191264

Xiaobo Song, Xu Zhang, Yuhua Zeng, Zheng Peng

引用次数: 0

Modelling influenza and SARS-CoV-2 interaction: Analysis for Catalonia region 模拟流感和SARS-CoV-2相互作用:加泰罗尼亚地区分析

Journal of Algorithms & Computational Technology Pub Date : 2023-01-01 DOI: 10.1177/17483026231186012

Pau Fonseca i Casas, Victor Garcia i Carrasco, Joan Garcia i Subirana

引用次数: 0

Statistical analysis of various splitting criteria for decision trees 决策树各种分裂准则的统计分析

Journal of Algorithms & Computational Technology Pub Date : 2023-01-01 DOI: 10.1177/17483026231198181

Fadwa Aaboub, Hasna Chamlal, Tayeb Ouaderhman

{"title":"Statistical analysis of various splitting criteria for decision trees","authors":"Fadwa Aaboub, Hasna Chamlal, Tayeb Ouaderhman","doi":"10.1177/17483026231198181","DOIUrl":"https://doi.org/10.1177/17483026231198181","url":null,"abstract":"Decision trees are frequently used to overcome classification problems in the fields of data mining and machine learning, owing to their many perks, including their clear and simple architecture, excellent quality, and resilience. Various decision tree algorithms are developed using a variety of attribute selection criteria, following the top-down partitioning strategy. However, their effectiveness is influenced by the choice of the splitting method. Therefore, in this work, six decision tree algorithms that are based on six different attribute evaluation metrics are gathered in order to compare their performances. The choice of the decision trees that will be compared is done based on four different categories of the splitting criteria that are criteria based on information theory, criteria based on distance, statistical-based criteria, and other splitting criteria. These approaches include iterative dichotomizer 3 (first category), C[Formula: see text] (first category), classification and regression trees (second category), Pearson’s correlation coefficient based decision tree (third category), dispersion ratio (third category), and feature weight based decision tree algorithm (last category). On eleven data sets, the six procedures are assessed in terms of classification accuracy, tree depth, leaf nodes, and tree construction time. Furthermore, the Friedman and post hoc Nemenyi tests are used to examine the results that were obtained. The results of these two tests indicate that the iterative dichotomizer 3 and classification and regression trees decision tree methods perform better than the other decision tree methodologies.","PeriodicalId":45079,"journal":{"name":"Journal of Algorithms & Computational Technology","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135002104","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

Pretrained back propagation based adaptive resonance theory network for adaptive learning 基于预训练反向传播的自适应共振理论网络自适应学习

Journal of Algorithms & Computational Technology Pub Date : 2023-01-01 DOI: 10.1177/17483026231205009

Caixia Zhang, Cong Jiang, Qingyang Xu

引用次数: 0

DataSifter II: Partially synthetic data sharing of sensitive information containing time-varying correlated observations. DataSifter II：包含时变相关观测值的敏感信息的部分合成数据共享。

IF 0.9

Journal of Algorithms & Computational Technology Pub Date : 2022-01-01 Epub Date: 2022-01-20 DOI: 10.1177/17483026211065379

Nina Zhou, Lu Wang, Simeone Marino, Yi Zhao, Ivo D Dinov

{"title":"DataSifter II: Partially synthetic data sharing of sensitive information containing time-varying correlated observations.","authors":"Nina Zhou, Lu Wang, Simeone Marino, Yi Zhao, Ivo D Dinov","doi":"10.1177/17483026211065379","DOIUrl":"10.1177/17483026211065379","url":null,"abstract":"There is a significant public demand for rapid data-driven scientific investigations using aggregated sensitive information. However, many technical challenges and regulatory policies hinder efficient data sharing. In this study, we describe a partially synthetic data generation technique for creating anonymized data archives whose joint distributions closely resemble those of the original (sensitive) data. Specifically, we introduce the DataSifter technique for time-varying correlated data (DataSifter II), which relies on an iterative model-based imputation using generalized linear mixed model and random effects-expectation maximization tree. DataSifter II can be used to generate synthetic repeated measures data for testing and validating new analytical techniques. Compared to the multiple imputation method, DataSifter II application on simulated and real clinical data demonstrates that the new method provides extensive reduction of re-identification risk (data privacy) while preserving the analytical value (data utility) in the obfuscated data. The performance of the DataSifter II on a simulation involving 20% artificially missingness in the data, shows at least 80% reduction of the disclosure risk, compared to the multiple imputation method, without a substantial impact on the data analytical value. In a separate clinical data (Medical Information Mart for Intensive Care III) validation, a model-based statistical inference drawn from the original data agrees with an analogous analytical inference obtained using the DataSifter II obfuscated (sifted) data. For large time-varying datasets containing sensitive information, the proposed technique provides an automated tool for alleviating the barriers of data sharing and facilitating effective, advanced, and collaborative analytics.","PeriodicalId":45079,"journal":{"name":"Journal of Algorithms & Computational Technology","volume":"16 ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/76/65/nihms-1800751.PMC9585991.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10553433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Message-passing implementation of the data diffusion communication model in fast multipole methods: large scale biomolecular simulations. 快速多极方法中数据扩散通信模型的消息传递实现:大规模生物分子模拟。

IF 0.9

Journal of Algorithms & Computational Technology Pub Date : 2008-01-01 DOI: 10.1260/174830108786231722

Jakub Kurzak, B Montgomery Pettitt

引用次数: 1