An Implementation of Logical Analysis of Data

By Endre Boros, Peter Ladislaw Hammer, T. Ibaraki, Alexander Kogan, E. Mayoraz, I. Muchnik. 2000

452 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Statistical Learning Theory

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Citations in the corpus

Interpretable Two-level Boolean Rule Learning for Classification (2015, methodology): “Second, Logical Analysis of Data (LAD) [2] learns patterns for both positive and negative samples by techniques such as set covering [11], and typically builds a classiﬁer by a weighted combination of the patterns, i.e. not a two-level rule.”
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules (2019, background): “Recently, there has been surge of effort to achieve balance between accuracy and rule size via principled objective functions and usage of combinatorial optimization techniques such as linear programming (LP) relaxations, sub-modular optimization, or Bayesian methods [2, 4, 14, 15, 27] 1.”
Learning customized and optimized lists of rules with mathematical programming (2018, methodology): “Some rule-based models are similar to the Logical Analysis of Data (LAD) framework [9], though the LAD model uses only rules that have confidence equal to one, so that even rules with confidence 0.”
MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules (2018, neutral): “Only recently there have been some formulations that attempt to balance the accuracy and the size of the rule in a principled optimization objective either through combinatorial optimization, linear programming (LP) relaxations, submodular optimization, or Bayesian methods [4,25,24][7,34] as we revi”
Boolean Decision Rules via Column Generation (2018, methodology): “Models produced by logical analysis of data [9, 31] from the operations research community are similarly weighted linear combinations.”
Scalable Bayesian Rule Lists (2016, background): “There are a series of works from the mid-1990’s onwards on finding optimal decision trees using dynamic programming and search techniques (e.g., Bennett & Blue, 1996; Auer et al., 1995; Dobkin et al., 1996; Boros et al., 2000; Garofalakis et al., 2000; Farhangfar et al., 2008), mainly working with f”
An Integer Optimization Approach to Associative Classification (2012, methodology): “To achieve this, we use association rules to build the modelsinto a type of decision list, that is, a rank ordered set of rules supported by data.”
Biclustering in data mining (2008, background): “Among them one should mention hierarchical clustering [5], k -means [6], self-organizing maps (SOM) [7], support vector machines (SVM) [8,9], logical analysis of data (LAD) [10,11], etc.”
ENDER: a statistical framework for boosting decision rules (2010, methodology): “Other algorithms based on Boolean reasoning and mathematical programming try to select the most relevant rules—this is the case of Logical Analysis of Data (Boros et al. 2000), where rules are called patterns.”
Integer optimization methods for machine learning (2012, methodology): “These models are similar to the Logical Analysis of Data (LAD) model [Boros et al., 2000], though the LAD model uses only rules that have confidence equal to one, so that even rules with confidence 0.”
Logical analysis of data—An overview: From combinatorial optimization to medical applications (2006, methodology): “The Logical Analysis of Data was originally developed for the analysis of datasets whose attributes take only binary (0-1) values (Hammer, 1986; Crama, Hammer, and Ibaraki, 1988; Boros et al., 2000 ).”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “In operations research, there is a field called “logical analysis of data" (Boros et al., 2000; Crama et al., 1988), which constructs rule sets based on combinatorics and optimization.”