Predictive Modeling for Classification

Part 2 of ebook "Applied predictive modeling" provides readers with contents including: Chapter 11 - Measuring performance in classification models; Chapter 12 - Discriminant analysis and other linear classification models; Chapter 13 - Nonlinear classification models; Chapter 14 - Classification trees and rule-based models; Chapter 15 - A Summary of grant application models; Chapter 16 - Remedies for severe class imbalance; Chapter 17 - Case study job scheduling; Chapter 18 - Measuring predictor importance; Chapter 19 - An introduction to feature selection; Chapter 20 - Factors that can af...

344p daonhiennhien 03-07-2024 1 1   Download

Part 1 of ebook "The elements of statistical learning: Data mining, inference, and prediction (Second edition)" provides readers with contents including: Chapter 1 - Introduction; Chapter 2 - Overview of supervised learning; Chapter 3 - Linear methods for regression; Chapter 4 - Linear methods for classification; Chapter 5 - Basis expansions and regularization; Chapter 6 - Kernel smoothing methods; Chapter 7 - Model assessment and selection; Chapter 8 - Model inference and averaging; Chapter 9 - Additive models, trees, and related methods;...

355p daonhiennhien 03-07-2024 2 1   Download

Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models.

16p vibransone 28-03-2024 4 2   Download

The paper presents a study on the application of basic machine learning models for churn customer classification. Churn prediction is an essential task in customer retention for businesses, and accurate identification of customers who are likely to churn can significantly impact the organization's revenue and customer satisfaction.

7p vibego 02-02-2024 4 1   Download

Part 2 book "Biostatistics - A methodology for the health sciences" includes content: Association and prediction - multiple regression analysis and linear models with multiple predictor variables; multiple comparisons; discrimination and classification; principal component analysis and factor analysis; rates and proportions; analysis of the time to an event - survival analysis; sample sizes for observational studies; longitudinal data analysis; randomized clinical trials; personal postscript.

453p muasambanhan06 03-02-2024 6 1   Download

Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of personalized diagnosis and treatment plans. This study aimed to build radiomics deep learning (DL) models using computed tomography (CT) data for the preoperative differential diagnosis of common cystic tumors of the pancreas.

10p vileonardodavinci 23-12-2023 6 3   Download

Continued part 1, part 2 of ebook "Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt (3rd edition)" provides readers with contents including: techniques for the classification and prediction of corporate financial distress and their applications; corporate credit scoring–insolvency risk models; an emerging market credit scoring system for corporates; application of distress prediction models; estimating recovery rates on defaulted debt;...

124p mocthanhdao0210 20-11-2023 8 4   Download

This book presents a sampling of work in the field. The primary target is the researcher or student who wishes to work in privacy-preserving data mining; the goal is to give a background on approaches along with details showing how to develop specific solutions within each approach. The book is organized much like a typical data mining text, with discussion of privacy-preserving solutions to particular data mining tasks.

123p haojiubujain06 23-10-2023 8 3   Download

The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decisionmaking for diagnosis, treatment and prognosis.

5p vighostrider 25-05-2023 5 2   Download

Clinical prediction models providing binary categorizations for clinical decision support require the selection of a probability threshold, or “cutpoint,” to classify individuals. Existing cutpoint selection approaches typically optimize test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification.

11p vighostrider 25-05-2023 3 2   Download

The aim of this study was to investigate the epidemiological characteristics and associated risk factors of recurrent lower-grade glioma [LGG] (WHO grades II and III) according to the 2016 updated WHO classification paradigm and finally develop a model for predicting early mortality (succumb within a year after reoperation) in recurrent LGG patients.

10p vipriyankagandhi 27-07-2022 5 2   Download

The study aims to apply the supervised machine learning method to the classification of product review content in online customer comment mining. The entire study was conducted automatic data collection with 2,241 customer reviews on products on Lazada.vn, then trained with Supervised Machine Learning models to find the most suitable model with the training dataset and apply this model to predict the reviews content for the dataset.

10p visherylsandberg 18-05-2022 14 1   Download

LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines.

24p viarchimedes 26-01-2022 6 0   Download

In this paper, we focus on the mention extraction and classification, proposing a neural-encoded mention-hypergraph model named the BILU-NEMH to extract the mention entities from a content. The proposed BILU-NEMH model combines a mention hypergraph model with the encoding schema and neural network.

12p guernsey 28-12-2021 7 0   Download

The novelty of our approach relies on a proper flow and fusion of information (DGHNL structure and its optimization). We show that the proposed DGHNL model with a 29-layer structure is capable to achieve the prediction accuracy of 94.60% (54 errors per 1000 classifications) for the Statlog German credit approval data.

18p guernsey 28-12-2021 8 1   Download

In this work, a new algorithm is proposed to predict both univariate and multivariate time series based on a combination of clustering, classification and forecasting techniques. The main goal of the proposed algorithm is first to group windows of time series values with similar patterns by applying a clustering process.

17p guernsey 28-12-2021 7 0   Download

In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naïve Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.

13p thiencuuchu 27-11-2021 15 1   Download

Proteins are a kind of macromolecules and the main component of a cell, and thus it is the most essential and versatile material of life. The research of protein functions is of great significance in decoding the secret of life. In recent years, researchers have introduced multi-label supervised topic model such as Labeled Latent Dirichlet Allocation (Labeled-LDA) into protein function prediction, which can obtain more accurate and explanatory prediction.

14p viseulgi2711 31-08-2021 8 1   Download

Researchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a single or multi-task classification problem; however, is critical and challenging. In this study, we proposed HetEnc, a novel deep learning-based approach, for information domain separation.

10p visilicon2711 20-08-2021 15 1   Download

Computer vision models have been proven to be productive as well as effective for concrete crack detection. This study develops an alternative model based on image edge detection, projection integral, and logistic regression approaches for recognizing and categorizing cracks on concrete surface. The integrated model has been developed using Visual C#.NET and tested with 200 real-world image samples. Experimental results point out that the new model has attained a good predictive performance with a classification accuracy of 92.5%.

7p nguaconbaynhay12 01-06-2021 24 1   Download