Towards a General Method for Logical Rule Extraction from Time Series

Sciavicco, Guido; Stan, Ionel Eduard; Vaccari, Alessandro

doi:10.1007/978-3-030-19651-6_1

Cited by 3 publications

(4 citation statements)

References 20 publications

(19 reference statements)

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“…The model does not generate a matrix during reduction and uses object equivalent class to get conflict and duplicate object sets, its time-space complexity is effectively controlled [7]. Sciavicco et al made use of a temporal logic language with a very high expressive power, and have designed a temporal abstraction algorithm that transforms time series into timelines so that temporal rules can be extracted with an already existing temporal generalization of APRIORI [8].…”

Section: Rule Extraction Methodsmentioning

confidence: 99%

Rule-based model for interpretable acute toxicity prediction

Zhong

Ren²,

Sun³

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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In recent years, machine learning algorithms had good performance in many fields. On the one hand, its predictive ability is greatly improved; on the other hand, with the increase of the model complexity, the interpretability of the algorithm is even worse. In this paper, we propose a novel method for improving the tree ensemble model by balancing predictive performance and interpretability. The rule extraction turns tree models into “if-then” rules. The rule pruning method removes the redundant constraints. And the rule selection method selects the optimal rule subset based on the genetic algorithm. An evaluation of the proposed method on the regression problem has been performed. Experiments on acute toxicity datasets demonstrate the effectiveness of the proposed approach.

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Section: Rule Extraction Methodsmentioning

confidence: 99%

Rule-based model for interpretable acute toxicity prediction

Zhong

Ren²,

Sun³

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

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“…The extraction of knowledge from time series is a problem studied in data mining. In [23] they do so by building an explicit model, a set of rules that are perfectly understandable to the expert, but without resorting to him or "ad-hoc" solutions. The proposed method creates timelines by abstraction from the time series, and from here to temporary rules using a known algorithm called APRIORI [24].…”

Section: A New Era For Artificial Intelligencementioning

confidence: 99%

“…The system efficiency is measured by Figure 19: Composition of the EEG signal from different frequencies (oscillatory activities). Five classical physiological bands are shown for the same raw EEG signal: delta (1-3 Hz); theta (46 Hz); alpha (7-13 Hz); beta (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25); and gamma (35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). These basic EEG bands are assumed to reflect different functional processes in the brain [155].…”

Section: Clinical Neurosciencementioning

confidence: 99%

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

et al. 2020

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Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in

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“…Typical examples of modal logics are temporal and spatial logics. Temporal symbolic learning has been first presented in this form in [13,43,44], where the authors study the use of a temporal modal logic (in particular, Halpern and Shoham's logic of time intervals, or H S [21]) in order to learn temporal decision trees for multivariate time-series classification. Among the several examples of multivariate time-series classification models that can be extracted with these techniques in the form of trees or sets of rules, consider the illuminating example of the medical context, in which patients under observation for a period of time may be classified by patterns of the form if there exists an interval in time in which the body temperature is higher than 39, overlapped by an interval where heart rate is higher than 140 then the patient has a certain condition.…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Learning with Spatial Modal Logics

Pagliarini

Sciavicco

2021

Electron. Proc. Theor. Comput. Sci.

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Symbolic learning represents the most straightforward approach to interpretable modeling, but its applications have been hampered by a single structural design choice: the adoption of propositional logic as the underlying language. Recently, more-than-propositional symbolic learning methods have started to appear, in particular for time-dependent data. These methods exploit the expressive power of modal temporal logics in powerful learning algorithms, such as temporal decision trees, whose classification capabilities are comparable with the best non-symbolic ones, while producing models with explicit knowledge representation. With the intent of following the same approach in the case of spatial data, in this paper we: (i) present a theory of spatial decision tree learning; (ii) describe a prototypical implementation of a spatial decision tree learning algorithm based, and strictly extending, the classical C4.5 algorithm, and (iii) perform a series of experiments in which we compare the predicting power of spatial decision trees with that of classical propositional decision trees in several versions, for a multi-class image classification problem, on publicly available datasets. Our results are encouraging, showing clear improvements in the performances from the propositional to the spatial models, which in turn show higher levels of interpretability.

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Towards a General Method for Logical Rule Extraction from Time Series

Cited by 3 publications

References 20 publications

Rule-based model for interpretable acute toxicity prediction

Rule-based model for interpretable acute toxicity prediction

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

Decision Tree Learning with Spatial Modal Logics

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