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E-raamat: Practical Guide to Averaging Functions

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This book offers an easy-to-use and practice-oriented reference guide to mathematical averages. It presents different ways of aggregating input values given on a numerical scale, and of choosing and/or constructing aggregating functions for specific applications. Building on a previous monograph by Beliakov et al. published by Springer in 2007, it outlines new aggregation methods developed in the interim, with a special focus on the topic of averaging aggregation functions. It examines recent advances in the field, such as aggregation on lattices, penalty-based aggregation and weakly monotone averaging, and extends many of the already existing methods, such as: ordered weighted averaging (OWA), fuzzy integrals and mixture functions. A substantial mathematical background is not called for, as all the relevant mathematical notions are explained here and reported on together with a wealth of graphical illustrations of distinct families of aggregation functions. The authors mainly foc

us on practical applications and give central importance to the conciseness of exposition, as well as the relevance and applicability of the reported methods, offering a valuable resource for computer scientists, IT specialists, mathematicians, system architects, knowledge engineers and programmers, as well as for anyone facing the issue of how to combine various inputs into a single output value.

Notations and Abbreviations.- Classical Averaging Functions.- Ordered Weighted Averaging.- Fuzzy Integrals.- Penalty Based Averages.- More Types of Averaging and Construction Methods.- Non-monotone Averages.- Averages on Lattices.
1 Review of Aggregation Functions
1(54)
1.1 Aggregation Functions
1(4)
1.2 Applications of Aggregation Functions
5(4)
1.3 Classification and General Properties
9(15)
1.3.1 Main Classes
9(1)
1.3.2 Main Properties
10(8)
1.3.3 Duality
18(3)
1.3.4 Comparability
21(1)
1.3.5 Continuity and Stability
21(3)
1.4 Main Families and Prototypical Examples
24(5)
1.4.1 Min and Max
24(1)
1.4.2 Means
24(1)
1.4.3 Medians
25(1)
1.4.4 Ordered Weighted Averaging
26(1)
1.4.5 Choquet and Sugeno Integrals
26(1)
1.4.6 Conjunctive and Disjunctive Functions
27(1)
1.4.7 Mixed Aggregation
28(1)
1.5 Composition and Transformation of Aggregation Functions
29(3)
1.6 How to Choose an Aggregation Function
32(3)
1.7 Supplementary Material: Some Methods for Approximation and Optimization
35(20)
1.7.1 Univariate Approximation and Smoothing
35(2)
1.7.2 Approximation with Constraints
37(1)
1.7.3 Multivariate Approximation
38(3)
1.7.4 Convex and Non-convex Optimization
41(6)
1.7.5 Main Tools and Libraries
47(3)
References
50(5)
2 Classical Averaging Functions
55(46)
2.1 Semantics
55(3)
2.1.1 Measure of Orness
56(2)
2.2 Classical Means
58(7)
2.2.1 Arithmetic Mean
58(7)
2.3 Weighted Quasi-arithmetic Means
65(17)
2.3.1 Definitions
65(1)
2.3.2 Main Properties
66(3)
2.3.3 Examples
69(4)
2.3.4 Calculation
73(1)
2.3.5 Weighting Triangles
73(3)
2.3.6 Weights Dispersion
76(1)
2.3.7 How to Choose Weights
77(5)
2.4 Other Means
82(19)
2.4.1 Gini Means
82(1)
2.4.2 Bonferroni Means
83(2)
2.4.3 Heronian Mean
85(1)
2.4.4 Generalized Logarithmic Means
86(2)
2.4.5 Cauchy and Lagrangean Means
88(1)
2.4.6 Mean of Bajraktarevic
89(1)
2.4.7 Mixture Functions
89(1)
2.4.8 Compound Means
90(1)
2.4.9 Extending Bivariate Means to More Than Two Arguments
91(6)
References
97(4)
3 Ordered Weighted Averaging
101(44)
3.1 Definitions
101(1)
3.2 Main Properties
102(4)
3.2.1 Orness Measure
103(2)
3.2.2 Entropy
105(1)
3.3 Other Types of OWA Functions
106(8)
3.3.1 Neat OWA
106(1)
3.3.2 Generalized OWA
107(2)
3.3.3 Weighted OWA
109(5)
3.4 How to Choose Weights in OWA
114(16)
3.4.1 Methods Based on Data
114(3)
3.4.2 Methods Based on a Measure of Dispersion
117(3)
3.4.3 Methods Based on Weight Generating Functions
120(3)
3.4.4 Fitting Weight Generating Functions
123(3)
3.4.5 Choosing Parameters of Generalized OWA
126(4)
3.5 Induced OWA
130(7)
3.5.1 Definition
130(1)
3.5.2 Properties
130(3)
3.5.3 Induced Generalized OWA
133(1)
3.5.4 Choices for the Inducing Variable
134(3)
3.6 Medians and Order Statistics
137(8)
3.6.1 Median
137(4)
3.6.2 Order Statistics
141(1)
References
141(4)
4 Fuzzy Integrals
145(38)
4.1 Choquet Integral
145(29)
4.1.1 Semantics
145(2)
4.1.2 Definitions and Properties
147(6)
4.1.3 Types of Fuzzy Measures
153(7)
4.1.4 Interaction, Importance and Other Indices
160(4)
4.1.5 Special Cases of the Choquet Integral
164(2)
4.1.6 Fitting Fuzzy Measures
166(7)
4.1.7 Generalized Choquet Integral
173(1)
4.2 Sugeno Integral
174(3)
4.2.1 Definition and Properties
174(1)
4.2.2 Special Cases
175(2)
4.3 Induced Fuzzy Integrals
177(6)
References
178(5)
5 Penalty Based Averages
183(24)
5.1 Motivation and Definitions
183(3)
5.2 Types of Penalty Functions
186(7)
5.2.1 Faithful Penalty Functions
186(2)
5.2.2 Restricted Dissimilarity Functions
188(3)
5.2.3 Minkowski Gauge Based Penalties
191(2)
5.3 Examples
193(4)
5.3.1 Quasi-arithmetic Means, OWA and Choquet Integral
193(1)
5.3.2 Deviation Means
194(1)
5.3.3 Entropic Means
195(1)
5.3.4 Bregman Loss Functions
195(2)
5.4 New Penalty Based Aggregation Functions
197(4)
5.5 Relation to the Maximum Likelihood Principle
201(2)
5.6 Representation of Averages
203(4)
References
204(3)
6 More Types of Averaging and Construction Methods
207(44)
6.1 Some Construction Methods
207(9)
6.1.1 Idempotization
207(1)
6.1.2 Means Defined by Using Graduation Curves
208(2)
6.1.3 Aggregation Functions with Flying Parameter
210(2)
6.1.4 Construction of Shift-Invariant Functions
212(1)
6.1.5 Interpolatory Constructions
212(4)
6.2 Other Types of Aggregation and Properties
216(2)
6.2.1 Bi-capacities
216(1)
6.2.2 Linguistic Aggregation Functions
217(1)
6.2.3 Multistage Aggregation
217(1)
6.2.4 Migrativity
218(1)
6.3 Overlap and Grouping Functions
218(9)
6.3.1 Definition of Overlap Functions and Basic Properties
219(1)
6.3.2 Characterization of Overlap Functions
220(1)
6.3.3 Homogeneous Overlap Functions
221(1)
6.3.4 k-Lipschitz Overlap Functions
222(2)
6.3.5 n-dimensional Overlap Functions
224(1)
6.3.6 Grouping Functions
225(2)
6.4 Generalized Bonferroni Mean
227(12)
6.4.1 Main Definitions
227(2)
6.4.2 Properties of the Generalized Bonferroni Mean
229(1)
6.4.3 Replacing the Outer Mean
230(1)
6.4.4 Replacing the Inner Mean
231(1)
6.4.5 Replacing the Product Operation
232(1)
6.4.6 Extensions to BKM
233(1)
6.4.7 Boundedness of the Generalized Bonferroni Mean
234(3)
6.4.8 k-intolerance Boundedness
237(1)
6.4.9 Generated t-norm and Generated Quasi-arithmetic Means as Components of BM
238(1)
6.5 Consistency and Stability
239(12)
6.5.1 Motivation
239(1)
6.5.2 Strictly Stable Families
240(1)
6.5.3 R-strict Stability
241(4)
6.5.4 Learning Consistent Weights
245(1)
6.5.5 Consistency and Global Monotonicity
246(1)
References
247(4)
7 Non-monotone Averages
251(54)
7.1 Motivation
251(1)
7.2 Weakly Monotone Functions
252(4)
7.2.1 Basic Properties of Weakly Monotone Functions
254(2)
7.3 Robust Estimators of Location
256(5)
7.3.1 Mode
257(1)
7.3.2 Shorth
257(1)
7.3.3 Least Median of Squares (LMS)
258(1)
7.3.4 Least Trimmed Squares (LTS)
258(1)
7.3.5 Least Trimmed Absolute Deviations (LTA)
259(1)
7.3.6 The Least Winsorized Squares Estimator
260(1)
7.3.7 OWA Penalty Functions
260(1)
7.4 Lehmer and Gini Means
261(4)
7.4.1 Lehmer Means
261(2)
7.4.2 Gini Means
263(2)
7.5 Mixture Functions
265(6)
7.5.1 Some Special Cases of Weighting Functions
266(1)
7.5.2 Affine Weighting Functions
267(2)
7.5.3 Linear Combinations of Weighting Functions
269(1)
7.5.4 The Duals of Lehmer Mean and Other Mixture Functions
270(1)
7.6 Density Based Means and Medians
271(4)
7.6.1 Density Based Means
271(1)
7.6.2 Density Based Medians and ML Estimators
272(1)
7.6.3 Modified Weighting Functions
273(2)
7.7 Mode-Like Averages
275(2)
7.8 Spatial-Tonal Filters
277(1)
7.9 Transforms
278(3)
7.10 Cone Monotone Functions
281(7)
7.10.1 Formal Definitions and Properties
281(4)
7.10.2 Verification of Cone Monotonicity
285(1)
7.10.3 Construction of Cone Monotone Lipschitz Functions
286(2)
7.11 Monotonicity with Respect to Coalitions
288(5)
7.11.1 Simple Majority
288(2)
7.11.2 Majority and Preferential Inputs
290(1)
7.11.3 Coalitions
291(2)
7.12 Directional Monotonicity
293(3)
7.12.1 Properties of r-Monotone functions
294(1)
7.12.2 The Set of Directions of Increasingness
295(1)
7.13 Pre-aggregation Functions
296(9)
7.13.1 Definitions and Properties
296(1)
7.13.2 Construction of Pre-aggregation Functions by Composition
297(2)
7.13.3 Choquet-Like Construction Method of Pre-aggregation Functions
299(1)
7.13.4 Sugeno-Like Construction Method of Pre-aggregation Functions
300(2)
References
302(3)
8 Averages on Lattices
305(42)
8.1 Aggregation of Intervals and Intuitionistic Fuzzy Values
305(22)
8.1.1 Preliminary Definitions
306(1)
8.1.2 Aggregation on Product Lattices
306(3)
8.1.3 Arithmetic Means and OWA for AIFV
309(4)
8.1.4 Alternative Definitions of Aggregation Functions on AIFV
313(3)
8.1.5 Consistency with Operations on Ordinary Fuzzy Sets
316(3)
8.1.6 Medians for AIFV
319(1)
8.1.7 Bonferroni Means
320(7)
8.2 Medians on Lattices
327(9)
8.2.1 Medians as Penalty Based Functions
327(1)
8.2.2 Median Graphs and Distributive Lattices
328(2)
8.2.3 Medians on Infinite Lattices and Fermat Points
330(1)
8.2.4 Medians Based on Distances Between Intervals
331(2)
8.2.5 Numerical Comparison
333(3)
8.3 Penalty Functions on Cartesian Products of Lattices
336(11)
References
342(5)
Index 347