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Handbook of Research on Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance

Over the last two decades, researchers are looking at imbalanced data learning as a prominent research area. Many critical real-world;

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Imbalanced Learning

Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet;

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Learning from Imbalanced Data Sets

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and;

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Learning from Imbalanced Data Sets

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and;

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Emerging Paradigms in Machine Learning

challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non;

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Statistical Methods for Imbalanced Data in Ecological and Biological Studies

. The book discusses the property of the imbalance of data from two points of view. The first is quantitative imbalance, meaning that the sample;

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Data Mining with Decision Trees

Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning;

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Advances in Knowledge Discovery and Data Mining

research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning;

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Advances in Knowledge Discovery and Data Mining

research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning;

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

mining and machine learning; three-way decisions; IJCRS 2015 data challenge.;

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Data Mining with SPSS Modeler

advanced data mining, and put this knowledge into practice. This revised and updated second edition includes a new chapter on imbalanced data and;

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Analysis Of Multivariate & High Dimensio

'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and;

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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

machine learning algorithms Book DescriptionMachine learning is applied everywhere, from business to research and academia, while scikit-learn;

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Mathematical Problems in Data Science

, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions;

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Machine Learning Research Progress

methods extract rules and patterns out of massive data sets. The major focus of machine learning research is to extract information from data;

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Data Mining

handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition;

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Machine Learning Approaches for Urban Computing

details multiple types of data generating from urban activities and suitability of various machine learning algorithms for handling urban data;

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Database Systems for Advanced Applications

selected from a total of 287 submissions. The papers cover the following topics: data mining; data streams and time series; database storage and;

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Transactions on Rough Sets XIII

The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical;

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Multiple Fuzzy Classification Systems

Fuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific;

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Improved Classification Rates for Localized Algorithms under Margin Conditions

other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and;

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Data Mining

, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and;

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Statistical and Machine Learning Approaches for Network Analysis

methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout;

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Statistical Learning for Big Dependent Data

Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting;

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Uncertainty Handling and Quality Assessment in Data Mining

fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning;

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Big Data of Complex Networks

Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural;

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Big Data of Complex Networks

Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural;

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