Statistical and Symbolic Neuroaesthetics Rules Extraction from EEG Signals

Coccagna, Maddalena; Manzella, F.; Mazzacane, Sante; Pagliarini, Giovanni; Sciavicco, Guido

doi:10.1007/978-3-031-06242-1_53

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…The theoretical properties of ModalCART have been studied in [18]. Such an integration is particularly useful when ML is replaced by tailored temporal/spatial, but still modal, logic, as in [13,32,36], for specific learning tasks. The theoretical aspects of such integration, however, are independent from the particular modal logic used for learning; as a matter of fact, modal decision trees can be designed in the same way for any unary modal logic.…”

Section: Preliminariesmentioning

confidence: 99%

“…Modal logic, which extends propositional logic, can be adopted by decision trees as the underlying logical language, in order to capture complex relationships between objects and their properties, ultimately enabling models to learn from unstructured data with no feature-representation embedding steps (i.e., representing unstructured data as structured). Modal decision trees have been successfully used in complex tasks such as time series knowledge extraction, image understanding, and knowledge graph reasoning [13,32,36]. In the same spirit of canonical ones, branches of modal decision trees represent modal logic formulas.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Explainable Rule Extraction from (Modal) Random Forests

Ghiotti,

Manzella,

Pagliarini

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Symbolic learning is the subfield of machine learning concerned with learning predictive models with knowledge represented in logical form, such as decision tree and decision list models. Ensemble learning methods, such as random forests, are usually deployed to improve the performance of decision trees; unfortunately, interpreting tree ensembles is challenging. In order to deal with unstructured (e.g., temporal or spatial) data, moreover, decision trees and random forests have been recently generalized to the use of modal logics, which are harder to interpret than their propositional counterpart. Recently, a methodology for extracting simple rules from propositional random forests, based on a sequence of optimization steps, was proposed. In this work, we generalize this approach along two directions: from propositional to modal logic and from a sequence of optimization steps to a single multi-objective optimization problem. Even if confined to the temporal domain, our experimental results, based on open-source implementations and public data, show that our method is robust and able to extract small, accurate, and informative decision lists even for complex classification problems.

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