A method for compiling general concepts with the use of temporal decision trees

Antipov, S. G.; Fomina, M. V.

doi:10.3103/s0147688211060025

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…A natural solution consists of augmenting D in such a way to simulate the behaviour of an infinite domain model. In our example, it suffices to consider D = {−2, −1, 0, 1, 2, 3, 4, 5}, so that a single split may be based on the rule: L f ever → C1, otherwise C2 holding on [−2, −1] (or, equivalently, its inverse formulation on [4,5]). Thus, the function AssignReferenceIntervals, while searching all possible reference intervals, takes into consideration two extra points at each side of the domain.…”

Section: Learning Interval Temporal Logic Decision Treesmentioning

confidence: 99%

“…Additionally, our approach should not be confused with [23], in which the term interval indicates an uncertain numerical value (e.g., the patient has a fever of 38 Celsius versus the patient has a fever between 37.5 and 38.5 Celsius), and in which an algorithm for inducing decision trees on such uncertain data is presented that is based on the so-called Kolmogorov-Smirnov criterion, but the data that are object of that study are not necessarily temporal, and the produced trees do not employ any temporal (logical) relation. In [4,29] and [21], the authors present two other approaches to a temporal generalization of decision tree learning. In the former, the authors provide a general method for building point-based temporal decision trees, but with no particular emphasis on any supporting formal language.…”

Section: Introductionmentioning

confidence: 99%

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Interval Temporal Logic Decision Tree Learning

Brunello

Sciavicco

Stan

2019

Lecture Notes in Computer Science

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Decision trees are simple, yet powerful, classification models used to classify categorical and numerical data, and, despite their simplicity, they are commonly used in operations research and management, as well as in knowledge mining. From a logical point of view, a decision tree can be seen as a structured set of logical rules written in propositional logic. Since knowledge mining is rapidly evolving towards temporal knowledge mining, and since in many cases temporal information is best described by interval temporal logics, propositional logic decision trees may evolve towards interval temporal logic decision trees. In this paper, we define the problem of interval temporal logic decision tree learning, and propose a solution that generalizes classical decision tree learning.

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Section: Learning Interval Temporal Logic Decision Treesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interval Temporal Logic Decision Tree Learning

Brunello

Sciavicco

Stan

2019

Lecture Notes in Computer Science

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“…[15]. Using the temporal constraint information, a fault can be diagnosed with specific algorithms, see [21,22].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Power System Fault Reasoning and Diagnosis Based on the Improved Temporal Constraint Network

Cui

Shi

Wang

2016

IEEE Trans. Power Delivery

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Temporal information is fundamental in model-based fault diagnosis (MBD), and the alarm processing problem is to interpret the alarm sequences to infer the type and time of fault event occurrences. There can be connected cycles or feedback loops in a real power system, but the fault reasoning methods for such cases are seldom considered in the literature. This paper provides an analytic model based on the improved temporal constraint network (ITCN). The reasoning method is dependent on time point and time distance information, with which the fault motivators (or actuators) and fault responders (or victims) can be identified. The system fault event reasoning and diagnosis are formulated as an optimization problem with the fault hypotheses (FHs) being tested. The calculation process consists of three steps: establishment of the objective function, determination of the fault propagation paths, and determination of the expected states under a given hypothesis. Finally, the proposed method is demonstrated via a power system example, which is verified to be successful on performance assessment and fault diagnosis.

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A method for compiling general concepts with the use of temporal decision trees

Cited by 2 publications

References 7 publications

Interval Temporal Logic Decision Tree Learning

Interval Temporal Logic Decision Tree Learning

Power System Fault Reasoning and Diagnosis Based on the Improved Temporal Constraint Network

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