Finding Trees in Mountains - Outlier Detection on Polygonal Chains

Outlier Detection by Boosting Regression Trees

A procedure for detecting outliers in regression problems is proposed. It is based on information provided by boosting regression trees. The key idea is to select the most frequently resampled observation along the boosting iterations and reiterate after removing it. The selection criterion is based on Tchebychev’s inequality applied to the maximum over the boosting iterations of ...

Identification of outliers types in multivariate time series using genetic algorithm

Multivariate time series data, often, modeled using vector autoregressive moving average (VARMA) model. But presence of outliers can violates the stationary assumption and may lead to wrong modeling, biased estimation of parameters and inaccurate prediction. Thus, detection of these points and how to deal properly with them, especially in relation to modeling and parameter estimation of VARMA m...

A Taxonomy Framework for Unsupervised Outlier Detection Techniques for Multi-Type Data Sets

The term “outlier” can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous application...

Finding Outlier DMUs in Data Envelopment Analysis

Outlier Detection Using Extreme Learning Machines Based on Quantum Fuzzy C-Means

One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structur...