Dirty Data Processing for Machine Learning / / by Zhixin Qi, Hongzhi Wang, Zejiao Dong

• Record Nr.: UNINA9910768172903321
• Autore: Qi Zhixin
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
• ISBN: 981-9976-57-X
• Edizione: [1st ed. 2024.]
• Descrizione fisica: 1 online resource (141 pages)
• Altri autori (Persone): WangHongzhi; DongZejiao
• Disciplina: 005.7
• Soggetti: Artificial intelligence - Data processing; Data mining; Big data; Data Science; Data Mining and Knowledge Discovery; Big Data
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Chapter 1. Introduction -- Chapter 2. Impacts of Dirty Data on Classification and Clustering Models -- Chapter 3. Dirty-Data Impacts on Regression Models -- Chapter 4. Incomplete Data Classification with View-Based Decision Tree -- Chapter 5. Density-Based Clustering for Incomplete Data -- Chapter 6. Feature Selection on Inconsistent Data -- Chapter 7. Cost-Sensitive Decision Tree Induction on Dirty Data.
• Sommario/riassunto: In both the database and machine learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as “dirty data.” Clearly, for a given data mining or machine learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing. Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high. If we knew how dirty data affected the accuracy of machine learning models, we could clean data selectively according to the accuracy requirements instead of cleaning all dirty data, which entails substantial costs. ; However, no book to date has studied the impacts of dirty data on machine learning models in terms of data quality. Filling precisely this gap, the book is intended for a broad audience ranging from researchers inthe database and machine learning communities to industry practitioners. Readers will find valuable takeaway suggestions on: model selection and data cleaning; incomplete data classification with view-based decision trees; density-based clustering for incomplete data; the feature selection method, which reduces the time costs and guarantees the accuracy of machine learning models; and cost-sensitive decision tree induction approaches under different scenarios. Further, the book opens many promising avenues for the further study of dirty data processing, such as data cleaning on demand, constructing a model to predict dirty-data impacts, and integrating data quality issues into other machine learning models. Readers will be introduced to state-of-the-art dirty data processing techniques, and the latest research advances, while also finding new inspirations in this field.

Causality : statistical perspectives and applications / / edited by Carlo Berzuini, Philip Dawid, Luisa Bernardinelli

• Record Nr.: UNINA9910820793703321
• Autore: Berzuini Carlo
• Pubbl/distr/stampa: Chichester, West Sussex, U.K., : Wiley, 2012
• ISBN: 9786613656162; 9781119941736; 1119941733; 9781280679230; 1280679239; 9781119945710; 1119945712; 9781119945703; 1119945704
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (415 p.)
• Collana: Wiley series in probability and statistics
• Classificazione: MAT029000
• Altri autori (Persone): BerzuiniCarlo; DawidPhilip; BernardinelliLuisa
• Disciplina: 519.5/44
• Soggetti: Estimation theory; Causation; Causality (Physics)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Statistical causality : some historical remarks -- The language of potential outcomes -- Structural equations, graphs and interventions -- The decision-theoretic approach to causal -- Causal inference as a prediction problem : assumptions, identification, and evidence synthesis -- Graph-based criteria of identifiability of causal questions -- Causal inference from observational data : a Bayesian predictive approach -- Causal inference from observing sequences of actions -- Causal effects and natural laws : towards a conceptualization of causal counterfactuals -- For non-manipulable exposures, with application to the effects of race and sex -- Cross-classifications by joint potential outcomes -- Estimation of direct and indirect effects -- The mediation formula : a guide to the assessment of causal pathways in nonlinear models -- The sufficient cause framework in statistics, philosophy and the biomedical and social sciences -- Inference about biological mechanism on the basis of epidemiological data -- Ion channels and multiple sclerosis -- Supplementary variables for causal estimation -- Time-varying confounding : some practical considerations in a likelihood framework -- Natural experiments as a means of testing causal inferences -- Nonreactive and purely reactive doses in observational studies -- Evaluation of potential mediators in randomized trials of complex interventions (psychotherapies) -- Causal inference in clinical trials -- Granger causality and causal inference in time series analysis -- Dynamic molecular networks and mechanisms iIn the biosciences : a statistical framework.
• Sommario/riassunto: "This book looks at a broad collection of contributions from experts in their fields"--