## Modeling Decisions: Information Fusion and Aggregation Operators (Google eBook)Information fusion techniques and aggregation operators produce the most comprehensive, specific datum about an entity using data supplied from different sources, thus enabling us to reduce noise, increase accuracy, summarize and extract information, and make decisions. These techniques are applied in fields such as economics, biology and education, while in computer science they are particularly used in fields such as knowledge-based systems, robotics, and data mining. This book covers the underlying science and application issues related to aggregation operators, focusing on tools used in practical applications that involve numerical information. Starting with detailed introductions to information fusion and integration, measurement and probability theory, fuzzy sets, and functional equations, the authors then cover the following topics in detail: synthesis of judgements, fuzzy measures, weighted means and fuzzy integrals, indices and evaluation methods, model selection, and parameter extraction. The methods are illustrated with representative examples throughout, and there are extensive bibliographies and reading suggestions. The book is intended for graduate students, researchers, and practitioners such as engineers, computer scientists, statisticians and economists who use decision models and aggregation operators. The reader is assumed to have a nonspecialized background in mathematics. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

XII | 21 |

XIII | 22 |

XIV | 24 |

XV | 25 |

XVI | 28 |

XVII | 30 |

XVIII | 33 |

XIX | 34 |

XX | 39 |

XXI | 45 |

XXII | 46 |

XXIII | 49 |

XXIV | 53 |

XXV | 58 |

XXVI | 59 |

XXVII | 60 |

XXVIII | 62 |

XXIX | 64 |

XXX | 67 |

XXXI | 69 |

XXXII | 74 |

XXXIII | 77 |

XXXIV | 79 |

XXXV | 81 |

XXXVII | 84 |

XXXVIII | 90 |

XXXIX | 93 |

XL | 97 |

XLI | 99 |

XLII | 101 |

XLIII | 102 |

XLIV | 103 |

XLV | 110 |

XLVI | 112 |

XLVII | 115 |

XLVIII | 118 |

XLIX | 120 |

L | 122 |

LI | 124 |

LII | 126 |

LIII | 127 |

LXIV | 154 |

LXV | 159 |

LXVI | 163 |

LXVII | 165 |

LXVIII | 167 |

LXIX | 171 |

LXX | 175 |

LXXIII | 180 |

LXXIV | 182 |

LXXVI | 187 |

LXXVII | 189 |

LXXVIII | 191 |

LXXIX | 197 |

LXXX | 198 |

LXXXI | 199 |

LXXXII | 200 |

LXXXIII | 201 |

LXXXIV | 203 |

LXXXVI | 204 |

LXXXVII | 205 |

LXXXVIII | 207 |

XC | 209 |

XCI | 212 |

XCII | 213 |

XCIII | 214 |

XCIV | 218 |

XCV | 220 |

XCVI | 223 |

XCVII | 224 |

XCVIII | 226 |

XCIX | 229 |

C | 235 |

CI | 236 |

CII | 237 |

CIII | 242 |

CIV | 243 |

CV | 244 |

CVI | 249 |

CVII | 251 |

CVIII | 253 |

### Common terms and phrases

Acz´el additive fuzzy measures aggregation operators algorithm alternative applied arithmetic mean Artiﬁcial Intelligence Banzhaf Chapter Choquet integral Cirat combination computed constraints corresponds decomposable fuzzy measure deﬁned deﬁned as follows Deﬁnition degree denote diﬀerent disjunction distorted probability elements entropy equal equivalent evaluation example expression f(xi ﬁeld Figure ﬁnite ﬁrst function f functional equations fuzzy integrals fuzzy quantiﬁer fuzzy sets given hierarchy implies importance inﬂuence information fusion information sources inputs interpretation Let us consider Let µ linear M¨obius transform Mathematical matrix maximum measure µ median membership function methods minimization monotone Murofushi node Note nullnorms obtained order statistics ordinal scales outcome OWA operator parameters particular properties Proposition quasi-arithmetic means random variable regression representation respect robust regression root-mean-powers satisﬁes satisfy Section Shapley value solution subsets Sugeno integral t-norms Theorem tion Torra twofold integral uninorms weighted mean weighted minimum weighting vector WOWA Yager