Open AccessBook
Knowledge Acquisition from Databases
Xindong Wu
- 01 Jan 1995
101
TL;DR: This is a textbook for undergraduate and postgraduate students on machine learning, expert systems, and artificial intelligence courses.
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Abstract: This is a textbook for undergraduate and postgraduate students on machine learning, expert systems, and artificial intelligence courses. The text may also serve as a reference book for researchers in machine learning, knowledge based systems, genetic algorithms, and neural networks.
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Citations
Classification in the Presence of Label Noise: A Survey
Benoît Frénay,Michel Verleysen +1 more
TL;DR: In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.
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Class noise vs. attribute noise: a quantitative study of their impacts
Xingquan Zhu,Xindong Wu +1 more
TL;DR: A systematic evaluation on the effect of noise in machine learning separates noise into two categories: class noise and attribute noise, and investigates the relationship between attribute noise and classification accuracy, the impact of noise at different attributes, and possible solutions in handling attribute noise.
985
A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises
TL;DR: A common misunderstanding of Gaussian-function-based kernel fuzzy clustering is corrected, and a kernel fuzzy c-means clustering-based fuzzy SVM algorithm (KFCM-FSVM) is developed to deal with the classification problems with outliers or noises.
Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition
TL;DR: The results obtained show that methods using the One-vs-One strategy lead to better performances and more robust classifiers when dealing with noisy data, especially with the most disruptive noise schemes.
Enabling Smart Data: Noise filtering in Big Data classification
TL;DR: In this article, two Big Data preprocessing approaches to remove noisy examples are proposed: an homogeneous ensemble and an heterogeneous ensemble filter, with special emphasis in their scalability and performance traits.
151