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Journal ArticleDOI

Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

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TLDR
This paper focuses on the use of nonparametric statistical inference for analyzing the results obtained in an experiment design in the field of computational intelligence, and presents a case study which involves a set of techniques in classification tasks.
About
This article is published in Information Sciences.The article was published on 2010-05-01. It has received 1737 citations till now. The article focuses on the topics: Statistical hypothesis testing & Friedman test.

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Citations
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Journal ArticleDOI

A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms

TL;DR: The basics are discussed and a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis are given.
Journal ArticleDOI

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

TL;DR: A taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based is proposed and a thorough empirical comparison is developed by the consideration of the most significant published approaches to show whether any of them makes a difference.
Proceedings Article

KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework

TL;DR: The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformat and some guidelines for including new algorithms in KEEL, helping the researcher to compare the results of many approaches already included within the KEEL software.
Journal ArticleDOI

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

TL;DR: A hybrid model where an unsupervised DBN is trained to extract generic underlying features, and a one-class SVM is trained from the features learned by the DBN, which delivers a comparable accuracy with a deep autoencoder and is scalable and computationally efficient.
References
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Book

Data Mining: Concepts and Techniques

TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Journal ArticleDOI

A Simple Sequentially Rejective Multiple Test Procedure

TL;DR: In this paper, a simple and widely accepted multiple test procedure of the sequentially rejective type is presented, i.e. hypotheses are rejected one at a time until no further rejections can be done.
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