scholarly journals Cross-migrative commutative aggregation functions with respect to Archimedean triangular norms

2019 ◽  
Author(s):  
Wenwen Zong ◽  
Yong Su ◽  
Liu Hua-Wen
Keyword(s):  
Triangular Norms ◽  
Aggregation Functions

Aggregation Functions

2009 ◽  
Author(s):  
Michel Grabisch ◽  
Jean-Luc Marichal ◽  
Radko Mesiar ◽  
Endre Pap
Keyword(s):  
Aggregation Functions

AGGREGATION FUNCTIONS WITH NON-MONOTONIC MEASURES

2008 ◽  
Vol 13 (02) ◽  
Author(s):  
Marta Cardin ◽  
Silvio Giove
Keyword(s):  
Aggregation Functions

scholarly journals Unsupervised Offline Changepoint Detection Ensembles

2021 ◽  
Vol 11 (9) ◽  
pp. 4280
Author(s):  
Iurii Katser ◽  
Viacheslav Kozitsin ◽  
Victor Lobachev ◽  
Ivan Maksimov
Keyword(s):  
Numerical Experiment ◽  
Failure Time ◽  
Cost Functions ◽  
Technical Diagnostics ◽  
Classification Problems ◽  
Tennessee Eastman Process ◽  
Changepoint Detection ◽  
Aggregation Functions ◽  
The Individual ◽  
Ensemble Algorithms

Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal’s changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach’s novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures—Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics.

scholarly journals Mixed aggregation functions for outliers detection

2021 ◽  
pp. 1-14
Author(s):  
Hengshan Zhang ◽  
Chunru Chen ◽  
Tianhua Chen ◽  
Zhongmin Wang ◽  
Yanping Chen
Keyword(s):  
Aggregation Function ◽  
Outliers Detection ◽  
Aggregation Functions ◽  
Novel Approach ◽  
Consensus Measure

A scenario that often encounters in the event of aggregating options of different experts for the acquisition of a robust overall consensus is the possible existence of extremely large or small values termed as outliers in this paper, which easily lead to counter-intuitive results in decision aggregation. This paper attempts to devise a novel approach to tackle the consensus outliers especially for non-uniform data, filling the gap in the existing literature. In particular, the concentrate region for a set of non-uniform data is first computed with the proposed searching algorithm such that the domain of aggregation function is partitioned into sub-regions. The aggregation will then operate adaptively with respect to the corresponding sub-regions previously partitioned. Finally, the overall aggregation is operated with a proposed novel consensus measure. To demonstrate the working and efficacy of the proposed approach, several illustrative examples are given in comparison to a number of alternative aggregation functions, with the results achieved being more intuitive and of higher consensus.

Continuous R-implications generated from representable aggregation functions

2010 ◽  
Vol 161 (17) ◽  
pp. 2276-2289 ◽  
Author(s):  
M. Carbonell ◽  
J. Torrens
Keyword(s):  
Aggregation Functions
Sign in / Sign up

Export Citation Format

Share Document