Data Preprocessing in Data Mining

By Salvador Garcia, Julián Luengo, Francisco Herrera. 2014

1,068 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

Citations in the corpus

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “Feature selection is overall an essential strategy in the data preprocessing [27, 22] phase.”
Learning to Recover 3D Scene Shape from a Single Image (2020, background): “Simple Min-Max normalization [14, 38] is sensitive to depth value outliers.”
An Overview of Deep Semi-Supervised Learning (2020, background): “Learning with Noisy Labels Learning from noisy labels [46, 52] can be challenging given the negative impact label noise can have on the performance of deep learning methods if the noise is significant.”
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis (2019, background): “Learning from noisy labels has been a long-standing challenge in machine learning [31], [32].”
Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review (2017, neutral): “ sample. 2.Attribute noise: refers to the corruptions in values of one or more attributes due to several causes, such as failures in sensor devices, irregularities in sampling or transcription errors [23]. The generation of noise can be classied by three main characteristics [64]: 1.The place where ”
Feature selection for high-dimensional data (2016, methodology): “[25] dedicated a chapter in their data preprocessing book to a discussion of feature selection and an analysis of itsmain aspects and methods.”
Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges (2022, methodology): “In this step, data for both training and online inference is shaped and formatted according to the input size of the specific AI/ML model being considered [126].”
Big Data and cloud computing: innovation opportunities and challenges (2017, background): “Large, high-dimensional spatiotemporal data cannot be managed by existing data reduction algorithms within a tolerable time frame and acceptable quality (Aghabozorgi, Seyed Shirkhorshidi, and Ying Wah 2015; García, Luengo, and Herrera 2015).”