检索结果-内蒙古大学图书馆

Personalized statistical learning algorithms to improve the early detection of cancer using longitudinal biomarkers

CANCER BIOMARKERS 2022年第2期33卷 199-210页

作者： Tayob, Nabihah Feng, Ziding Dana Farber Canc Inst Dept Data Sci Boston MA 02115 USA Harvard Med Sch Dept Med Boston MA 02115 USA Fred Hutchinson Canc Res Ctr Publ Hlth Sci Div Biostat Program 1124 Columbia St Seattle WA 98104 USA

BACKGROUND: Patients undergoing screening for early detection of cancer have serial biomarker measurements that are not traditionally being incorporated into decision making when evaluating biomarkers. OBJECTIVE: We discuss statistical learning algorithms that have the ability to learn from patient history to make personalized decision rules to improve the early detection of cancer. These artificial intelligence algorithms are able to learn in real time from data collected on the patient to identify changes in the patient that could signal asymptomatic cancer. METHODS: We discuss the parametric empirical Bayes (PEB) algorithm for a single biomarker and a Bayesian screening algorithm for multiple biomarkers. RESULTS: We provide tools to implement these algorithms and discuss their clinical utility for the early detection of hepatocellular carcinoma (HCC). The PEB algorithm is a robust, easily implemented algorithm for defining patient specific thresholds that can improve the patient-level sensitivity of a biomarker in many settings, including HCC. The fully Bayesian algorithm, while more complex, can accommodate multiple biomarkers and further improve the clinical utility of the algorithms. CONCLUSIONS: These algorithms could be used in many clinical settings and we aim to guide the reader on how these algorithms may improve the detection performance of their biomarkers.

关键词： statistical learning algorithms cancer biomarkers early detection parametric empirical Bayes Bayesian changepoint models

来源：评论

学校读者我要写书评

暂无评论

The impact of governance structure on bank performance: A cross-country panel analysis using statistical learning algorithms

引用

Procedia Computer Science 2022年 203卷 520-524页

作者： Oumniya Amrani Amal Najab Mohamed Azmi LARCEPEM CIRPEC FSJES-Souissi Mohamed V University Rabat Morocco ENSAM Hassan II University Casablanca Morocco

This paper investigates the relationship between governance structure and bank performance in normal and crisis times. Using statistical learning algorithms on R, we regressed the profitability indicators as dependent variables on board structure, bank-specific characteristics, and macroeconomic variables to examine the impact on profitability for 76 banks over 17 countries for the period from 2007 to 2019, including normal periods and the subprime crisis time. The findings show that the duality between CEO and chair has a negative and significant impact on bank performance during the full study period. BoD size and gender diversity within the BoD have no influence on bank performance but in crisis time, a woman CEO has a positive and significant impact on bank's performance.

关键词： Governance structure bank performance panel analysis statistical learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Evaluating machine learning and statistical learning techniques for cancer classification and diagnosis

引用

Iran Journal of Computer Science 2025年 1-20页

作者： Alwazy, Asmaa Salim Hussaien Buyrukoğlu, Gonca Buyrukoğlu, Selim Baker, Mohammed Rashad Department of Electronics and Computer Engineering Çankırı Karatekin University Çankırı 18100 Türkiye Department of Statistics Çankırı Karatekin University Çankırı 18100 Türkiye Department of Computer Engineering Çankırı Karatekin University Çankırı 18100 Türkiye Department of Software Engineering University of Kirkuk Kirkuk Iraq

Accurate cancer diagnosis is critical for effective treatment and positive patient outcomes. This study investigates the robustness of machine learning (ML) and statistical learning (SL) algorithms in classifying and diagnosing lung, prostate, breast, and heart disease. We evaluated and compared the performance of several algorithms, including support vector classifier (SVC), random forest (RF), XGBoost, decision tree (DT), elastic net, Lasso, and ridge, using four medical data sets. Multiple metrics, such as accuracy, precision, recall, F1-score, and area under the curve (AUC), are used to assess the performance. Hyperparameter tuning is conducted using GridSearchCV. The results demonstrated that RF and SVC achieved near-perfect accuracy (up to 98%) and AUC scores (1.00) in distinguishing between benign and malignant lung and breast cancer samples. SL algorithms exhibited robust and consistent performance for prostate cancer, achieving accuracy, precision, recall, and F1-scores of approximately 90%, making them suitable for data sets with smaller sample sizes. However, SVC-based ML algorithms outperformed SL methods in overall accuracy for prostate cancer, although SL methods provided a stable baseline. For heart disease, SVC achieved the highest accuracy (82%), while regularized SL models such as Elastic Net and Lasso performed similarly, with an accuracy of 80%. This study highlights the importance of selecting algorithms tailored to the data set’s characteristics and the specific diagnostic requirements. It also underscores the complementary strengths of ML and SL approaches, suggesting that an integrated strategy may offer the most effective solution. The findings contribute to the growing evidence supporting integrating computational methods into clinical practice to enhance diagnostic accuracy and improve patient care. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cancer classification Data mining Diagnosis Machine learning algorithms statistical learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Unsupervised statistical learning applied to experimental high-energy physics and related areas

引用

INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2016年第5期27卷 1630002-1630002页

作者： Simas Filho, Eduardo F. Seixas, Jose M. Univ Fed Bahia Elect Engn Program Rua Aristides Novis 02 BR-40210630 Salvador BA Brazil Univ Fed Rio de Janeiro Signal Proc Lab COPPE Poli BR-21941 Rio De Janeiro Brazil

Unsupervised statistical learning (USL) techniques, such as self-organizing maps (SOMs), principal component analysis (PCA) and independent component analysis explore different statistical properties to efficiently process information from multiple variables. USL algorithms have been successfully applied in experimental high-energy physics (HEP) and related areas for different purposes, such as feature extraction, signal detection, noise reduction, signal-background separation and removal of cross-interference from multiple signal sources in multisensor measurement systems. This paper presents both a review of the theoretical aspects of these signal processing methods and examples of some successful applications in HEP and related areas experiments.

关键词： statistical learning algorithms experimental high-energy physics signal processing data analysis

来源：评论

学校读者我要写书评

暂无评论

On learning mu-perceptron networks on the uniform distribution

引用

NEURAL NETWORKS 1996年第1期9卷 67-82页

作者： Golea, M Marchand, M Hancock, TR UNIV OTTAWA DEPT COMP SCIOTTAWAON K1N 6N5CANADA SIEMENS CORP RES PRINCETONNJ

We investigate the learnability, under the uniform distribution, of neural concepts that can be represented as simple combinations of nonoverlapping perceptrons (also called mu-perceptrons) with binary weights and arbitrary thresholds. Two perceptrons are said to be nonoverlapping if they do not share any input variables. Specifically, we investigate, within the distribution-specific PAC model, the learnability of mu-perceptron unions, decision lists, and generalized decision lists. In contrast to most neural network learning algorithms, we do not assume that the architecture of the network is known in advance. Rather, it is the task of the algorithm to find both the architecture of the net and the weight values necessary to represent the function to be learned. We give polynomial time algorithms for learning these restricted classes of networks. The algorithms work by estimating various statistical quantities that yield enough information to infer, with high probability, the target concept. Because the algorithms are statistical in nature, they are robust against large amounts of random classification noise.

关键词： nonoverlapping neural networks halfspace unions neural decision lists PAC learning read-once formula statistical learning algorithms learning on the uniform distribution

来源：评论

学校读者我要写书评

暂无评论

Forecasting with genetically programmed polynomial neural networks

引用

INTERNATIONAL JOURNAL OF FORECASTING 2006年第2期22卷 249-265页

作者： de Menezes, LM Nikolaev, NY City Univ London Fac Management Cass Business Sch London EC1V 0HB England Univ London Univ London Goldsmiths Coll Dept Comp London England

Recent literature on nonlinear models has shown genetic programming to be a potential tool for forecasters. A special type of genetically programmed model, namely polynomial neural networks, is addressed. Their outputs are polynomials and, as such, they are open boxes that are amenable to comprehension, analysis, and interpretation. This paper presents a polynomial neural network forecasting system, PGP, which has three innovative features: polynomial block reformulation, local ridge regression for weight estimation, and regularized weight subset selection for pruning that uses a least absolute shrinkage and selection operator. The relative performance of this system to other established forecasting procedures is the focus of this research and is illustrated by three empirical studies. Overall, the results are very promising and indicate areas for further research. (c) 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

关键词： nonlinear models tree-structured polynomial neural network models statistical learning algorithms genetic programming

来源：评论

学校读者我要写书评

暂无评论

An evaluation of statistical approaches to text categorization

引用

Information Retrieval 1999年第1-2期1卷 69-90页

作者： Yang, Yiming School of Computer Science Carnegie Mellon University Pittsburgh PA 15213-3702 United States

This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classifiers, kNN, LLSF and WORD, was conducted to examine the impact of configuration variations in five versions of Reuters on the observed performance of classifiers. Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were significantly affected by the inclusion of a large portion of unlabelled documents, mading those results difficult to interpret and leading to considerable confusions in the literature. Using the results evaluated on the other versions of Reuters which exclude the unlabelled documents, the performance of twelve methods are compared directly or indirectly. For indirect compararions, kNN, LLSF and WORD were used as baselines, since they were evaluated on all versions of Reuters that exclude the unlabelled documents. As a global observation, kNN, LLSF and a neural network method had the best performance;except for a Naive Bayes approach, the other learning algorithms also performed relatively well. © 1999 Kluwer Academic Publishers.

关键词： Comparative study Evaluation statistical learning algorithms Text categorization

来源：评论

学校读者我要写书评

暂无评论

Using empirical wavelet transform and high-order fuzzy cognitive maps for time series forecasting

引用

APPLIED SOFT COMPUTING 2023年 135卷

作者： Mohammadi, Hossein Abbasian Ghofrani, Sedigheh Nikseresht, Ali Amirkabir Univ Technol Elect Engn Dept Tehran Iran Islamic Azad Univ Elect & Elect Engn Dept South Tehran Branch Tehran Iran Shahid Beheshti Univ Fac Management & Accounting Dept Business Adm Tehran Iran

Many studies on time series forecasting have employed fuzzy cognitive maps (FCMs). However, it is required to develop techniques capable of effective responses and great accuracy for large-scale non -stationary time series, such as sharp-fluctuated datasets. For forecasting such data, the present study introduces an accurate, precise, and efficient combined forecasting framework with ridge regression, high-order FCM (HFCM), and empirical wavelet transform (EWT). The proposed learning method named as EWTHFCM. This model employs EWT for non-stationary time series transformation into a multivariate sequence. The HFCM then models each multivariate time series through a node. For the optimized learning part, ridge regression is employed to perform the process for the HFCM representation of large-scale time series. The main reasons to choose the ridge regression are its circle -like phenomenon which makes it a perfect fit for being combined with EWT. This fitness is due to the optimal filter bank selection behavior and adaptivity feature of EWT, eventually making the ridge regression fast, efficient, and accurate. Subsequently, HFCM models and forecasts the multivariate time series and helps find the trend pattern. Then, the multivariate time series is reconstructed by inverse EWT to predict time series at all time steps. The root mean square error (RMSE) is measured on 15 real-life standard datasets, of which 8 are utilized to compare the proposed framework to 11 state-of -art algorithms. According to the experimental results and comparison with other research papers, we recommend using EWTHFCM as a new, superior, and accurate method for many forecasting purposes and circumstances. (c) 2023 Elsevier B.V. All rights reserved.

关键词： High-order fuzzy cognitive maps Empirical wavelet transform Time series forecasting statistical learning algorithms Non-stationary dataset

来源：评论

学校读者我要写书评

暂无评论

Copula-based transferable models for synthetic population generation

引用

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 169卷

作者： Jutras-Dube, Pascal Al-Khasawneh, Mohammad B. Yang, Zhichao Bas, Javier Bastin, Fabian Cirillo, Cinzia Purdue Univ Dept Comp Sci 305 N Univ St W Lafayette IN 47907 USA Univ Montreal Dept Comp Sci & Operat Res CP 6128 Montreal PQ H3C 3J7 Canada Univ Montreal CIRRELT CP 6128 Montreal PQ H3C 3J7 Canada Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA Univ Autonoma Madrid Dept Quantitat Econ Madrid 28049 Spain

Population synthesis involves generating synthetic yet realistic representations of a target population of micro-agents for behavioral modeling and simulation. Traditional methods, often reliant on target population samples, such as census data or travel surveys, face limitations due to high costs and small sample sizes, particularly at smaller geographical scales. We propose a novel framework based on copulas to generate synthetic data for target populations where only empirical marginal distributions are known. This method utilizes samples from different populations with similar marginal dependencies, introduces a spatial component into population synthesis, and considers various information sources for more realistic generators. Concretely, the process involves normalizing the data and treating it as realizations of a given copula, and then training a generative model before incorporating the information on the marginals of the target population. Utilizing American Community Survey data, we assess our framework's performance through standardized root mean squared error (SRMSE) and so-called sampled zeros. We focus on its capacity to transfer a model learned from one population to another. Our experiments include transfer tests between regions at the same geographical level as well as to lower geographical levels, hence evaluating the framework's adaptability in varied spatial contexts. We compare Bayesian Networks, Variational Autoencoders, and Generative Adversarial Networks, both individually and combined with our copula framework. Results show that the copula enhances machine learning methods in matching the marginals of the reference data. Furthermore, it consistently surpasses Iterative Proportional Fitting in terms of SRMSE in the transferability experiments, while introducing unique observations not found in the original training sample.

关键词： Population synthesis Generative modeling Model transferability statistical learning algorithms Copulas Small areas

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：