Journal ArticleDOI
Lexicographic preferences for predictive modeling of human decision making: A new machine learning method with an application in accounting
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TLDR
This paper introduces a learning algorithm for inducing generalized lexicographic preference models from a given set of training data, which consists of pairwise comparisons between objects, and generalizes simpleLexicographic orders in the sense of allowing the model to consider several attributes simultaneously (instead of looking at them one by one), thereby significantly increasing the expressiveness of the model class.About:
This article is published in European Journal of Operational Research.The article was published on 2017-04-01. It has received 20 citations till now. The article focuses on the topics: Lexicographic preferences & Preference learning.read more
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Noncompensatory and generalized noncompensatory preference structures
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Inverse multiple criteria sorting problem
TL;DR: This study considers the inverse multiple criteria sorting problem (IMCSP), in which it is possible to perform actions which have an impact of objects evaluations, hence on the objects classification, and develops a resolution method based on mathematical programming models.
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Discretionary accounting choices: the case of IAS 19 pension accounting
TL;DR: In this article, the authors investigate how stock-listed companies in France, Germany and the UK use two discretionary choices in their accounting for defined benefit pension plans under International Accounting Standard (IAS) 19 Employee Benefits.
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Voting on multi-issue domains with conditionally lexicographic preferences
TL;DR: This paper addresses the computation of the winning alternative for some important rules, both by identifying the computational complexity of the relevant problems and by showing that for several of them, computing the winner reduces in a very natural way to a maxsat problem.
References
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Proceedings Article
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Usama M. Fayyad,Keki B. Irani +1 more
TL;DR: This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous-valued attribute into multiple intervals.
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Reasoning the fast and frugal way: models of bounded rationality.
TL;DR: The authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making, and found that these fast and frugal algorithms violate fundamental tenets of classical rationality: they neither look up nor integrate all information.
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The Economic Consequences of Increased Disclosure
TL;DR: In this paper, the authors study German firms that have switched from the German to an international reporting regime (1AS or U.S. GAAP) and show that proxies for the information asymmetry component of the cost of capital for the switching firms, namely, the bid-ask spread and trading volume behave in the predicted direction compared to firms employing the German reporting regime.
Proceedings ArticleDOI
Training linear SVMs in linear time
TL;DR: A Cutting Plane Algorithm for training linear SVMs that provably has training time 0(s,n) for classification problems and o(sn log (n)) for ordinal regression problems and several orders of magnitude faster than decomposition methods like svm light for large datasets.