scholarly journals Fault Diagnosis and Knowledge Extraction Using Fast Logical Analysis of Data with Multiple Rules Discovery Ability

2018 ◽  
pp. 412-421
Author(s):  
Xiwei Bai ◽  
Jie Tan ◽  
Xuelei Wang
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Extraction ◽  
Logical Analysis ◽  
Logical Analysis Of Data

Fault diagnosis in industrial chemical processes using interpretable patterns based on Logical Analysis of Data

2018 ◽  
Vol 95 ◽  
pp. 368-383 ◽  
Author(s):  
Ahmed Ragab ◽  
Mohamed El-Koujok ◽  
Bruno Poulin ◽  
Mouloud Amazouz ◽  
Soumaya Yacout
Keyword(s):  
Fault Diagnosis ◽  
Chemical Processes ◽  
Logical Analysis ◽  
Logical Analysis Of Data ◽  
Industrial Chemical

Fault diagnosis in power transformers using multi-class logical analysis of data

2013 ◽  
Vol 25 (6) ◽  
pp. 1429-1439 ◽  
Author(s):  
Mohamad-Ali Mortada ◽  
Soumaya Yacout ◽  
Aouni Lakis
Keyword(s):  
Fault Diagnosis ◽  
Logical Analysis ◽  
Power Transformers ◽  
Logical Analysis Of Data

Logical Analysis of Data-A Survey Paper

2017 ◽  
Vol 7 (3) ◽  
pp. 227-229
Author(s):  
Himani Chauhan ◽  
◽  
Garima Saxena ◽  
Arpit Tripathi ◽  
◽  
...  
Keyword(s):  
Logical Analysis ◽  
Logical Analysis Of Data ◽  
Survey Paper

COMPARISON OF TWO OPTIMIZATION MODELS FOR CONSTRUCTING PATTERNS IN THE METHOD OF LOGICAL ANALYSIS OF DATA

2020 ◽  
pp. 10-13
Author(s):  
Р.И. Кузьмич ◽  
А.А. Ступина ◽  
С.Н. Ежеманская ◽  
А.П. Шугалей
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Logical Analysis ◽  
Optimization Models ◽  
Logical Analysis Of Data

Предлагаются две оптимизационные модели для построения информативных закономерностей. Приводится эмпирическое подтверждение целесообразности использования критерия бустинга в качестве целевой функции оптимизационной модели для получения информативных закономерностей. Информативность, закономерность, критерий бустинга, оптимизационная модель Comparison of two optimization models for constructing patterns in the method of logical analysis of data Two optimization models for constructing informative patterns are proposed. An empirical confirmation of the expediency of using the boosting criterion as an objective function of the optimization model for obtaining informative patterns is given.

scholarly journals A decomposability index in logical analysis of data

2004 ◽  
Vol 142 (1-3) ◽  
pp. 165-180 ◽  
Author(s):  
Hirotaka Ono ◽  
Mutsunori Yagiura ◽  
Toshihide Ibaraki
Keyword(s):  
Logical Analysis ◽  
Logical Analysis Of Data

REDUCTION OF THE CLASSIFIER IN THE METHOD OF LOGICAL ANALYSIS OF DATA BASED ON THE Ε-, Δ-CRITERION FOR SELECTING PATTERN

2021 ◽  
pp. 4-8
Author(s):  
Р.И. Кузьмич ◽  
А.А. Ступина ◽  
В.А. Соколов ◽  
И.С. Поважнюк
Keyword(s):  
Training Sample ◽  
Logical Analysis ◽  
Logical Analysis Of Data ◽  
Sample Application ◽  
Subsequent Selection ◽  
Selection Of ◽  
Algorithmic Procedure

Предлагается алгоритмическая процедура редукции классификатора в методе логического анализа данных, основанная на отборе закономерностей с помощью ε-, δ-критерия. Реализация подхода заключается в формировании исходного классификатора как набора закономерностей на базе наблюдений обучающей выборки, применения к полученным правилам процедуры наращивания и последующего их отбора в новый классификатор на базе ε-, δ-критерия. Приводится эмпирическое подтверждение целесообразности данной алгоритмической процедуры. An algorithmic procedure for the reduction of the classifier in the method of logical analysis of data, based on the selection of patterns using the ε-, δ-criterion is proposed. The implementation of the approach consists in the formation of the initial classifier as a set of patterns based on observations of the training sample, application of the increasing procedure to the obtained patterns and their subsequent selection into a new classifier based on the ε-, δ-criterion. An empirical confirmation of the expediency of this algorithmic procedure is given.

Logical Analysis of Data

2011 ◽  
pp. 689-692 ◽  
Author(s):  
Endre Boros ◽  
Peter L. Hammer ◽  
Toshihide Ibaraki
Keyword(s):  
Data Mining ◽  
Boolean Function ◽  
Categorical Data ◽  
Boolean Functions ◽  
Binary Data ◽  
Logical Form ◽  
Logical Analysis ◽  
Logical Analysis Of Data ◽  
Primary Target ◽  
The Given

The logical analysis of data (LAD) is a methodology aimed at extracting or discovering knowledge from data in logical form. The first paper in this area was published as Crama, Hammer, & Ibaraki (1988) and precedes most of the data mining papers appearing in the 1990s. Its primary target is a set of binary data belonging to two classes for which a Boolean function that classifies the data into two classes is built. In other words, the extracted knowledge is embodied as a Boolean function, which then will be used to classify unknown data. As Boolean functions that classify the given data into two classes are not unique, there are various methodologies investigated in LAD to obtain compact and meaningful functions. As will be mentioned later, numerical and categorical data also can be handled, and more than two classes can be represented by combining more than one Boolean function.

An Optimal Equipment Replacement Model Using Logical Analysis of Data

2015 ◽  
Vol 6 (2) ◽  
pp. 83-96 ◽  
Author(s):  
Alireza Ghasemi ◽  
Sasan Esmaeili
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Programming Model ◽  
Health Management ◽  
Logical Analysis ◽  
Data Repository ◽  
Logical Analysis Of Data ◽  
Equipment Replacement ◽  
Hazards Model ◽  
Replacement Model

In this study, Logical Analysis of Data (LAD) is used to propose an optimal equipment replacement model. Unlike most classification techniques, LAD has the advantage of not relying on any statistical theory which enables it to overcome the conventional problems concerning the statistical properties of datasets. LAD is employed to estimate the equipment's survival and failure probabilities. These probabilities are then used to build a dynamic programming model to minimize the average long-term replacement cost of the equipment. The proposed method is successfully applied on Prognostics and Health Management challenge dataset provided by NASA Ames Prognostics Data Repository. The performance of the model is compared to that of the well-known Proportional Hazards Model.

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