A solution to the classification problem with cellular automata

Uzun, Arif Orhun; Usta, Tugba; Dündar, Enes Burak; Korkmaz, Emin Erkan

doi:10.1016/j.patrec.2018.10.007

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…CARS process uses a cellular automata model, which also incorporates a patchgeneration mechanism based on multi-type random seeds of land uses (Liang et al, 2020). The cellular automaton is a method where each cell interacts with neighboring cells based on a set of predefined rules simulating self-replicating complex systems that discretize both space and time that was proposed by von Neumann and Ulam in the late 1940s (Fuyong et al, 2020;Liao et al, 2019;Uzuna et al, 2018).…”

Section: Calibration and Validationmentioning

confidence: 99%

Modeling Future Impacts on Land Cover of Rapid Expansion of Hazelnut Orchards: A Case Study on Samsun, Turkey

Aytaç

2022

EUR J SUSTAIN DEV RES

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Land-use/land-cover (LULC) simulation models predict the long-term effects of LULC changes under various scenarios. Patch-level land use simulation (PLUS) is a recently developed software that uses a rule-mining framework for LULC modelling. With a market share of 76% in the world, hazelnut is a strategic crop for Turkey. The hazelnut orchards have grown in Turkey since the first law was issued on 21 October 1935. This study was carried out to model the hazelnut orchards for 2030, 2042, 2054, and 2066 based on Samsun province and show the future impacts on land use types. Samsun was chosen as a case study due to the rapid expansion of hazelnut groves since 2006. According to PLUS results, by the year 2030, the increase in the hazelnut groves in Samsun is predicted as 9.38%, and hazelnut fields will be formed by the main transformation of open spaces with little or no vegetation, shrub and/or herbaceous vegetation associations, and forest; and this transformation will have severe effects on the ecosystem. The results clearly showed that hazelnut cultivation areas would continue to increase in the future and revealed that policymakers would need to conduct new regulations for environmental sustainability and to maintain Turkey's power in this crop.

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Section: Calibration and Validationmentioning

confidence: 99%

Modeling Future Impacts on Land Cover of Rapid Expansion of Hazelnut Orchards: A Case Study on Samsun, Turkey

Aytaç

2022

EUR J SUSTAIN DEV RES

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“…Scheduling is another field where CA has been profusely in use (Carvalho and Carneiro 2019). Another recent CA approach for classification is Uzun et al (2018). CA have been also used with convolutional neural networks (Gilpin 2019) and reservoir computing (Nichele and Molund 2017).…”

Section: Cellular Automata For Pattern Recognitionmentioning

confidence: 99%

CURIE: a cellular automaton for concept drift detection

Lobo¹,

Ser

Osaba³

et al. 2021

Data Min Knowl Disc

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Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose CURIE, a drift detector relying on cellular automata. Specifically, in CURIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that CURIE, when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. CURIE is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.

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“…The TM builds upon a long tradition of LA research, involving cooperating systems of LA [14,13,12,11]. More recently, LA have been combined with cellular automata (CA), where each CA cell contains one or more LA, which learn in a distributed fashion [6,3,16]. Some noteworthy LA-based classifiers are further introduced in [4,7,1,18,2,17].…”

Section: Introductionmentioning

confidence: 99%

A Tsetlin Machine with Multigranular Clauses

Gorji¹,

Granmo²,

Phoulady³

et al. 2019

Preprint

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The recently introduced Tsetlin Machine (TM) has provided competitive pattern recognition accuracy in several benchmarks, however, requires a 3-dimensional hyperparameter search. In this paper, we introduce the Multigranular Tsetlin Machine (MTM). The MTM eliminates the specificity hyperparameter, used by the TM to control the granularity of the conjunctive clauses that it produces for recognizing patterns. Instead of using a fixed global specificity, we encode varying specificity as part of the clauses, rendering the clauses multigranular. This makes it easier to configure the TM because the dimensionality of the hyperparameter search space is reduced to only two dimensions. Indeed, it turns out that there is significantly less hyper-parameter tuning involved in applying the MTM to new problems. Further, we demonstrate empirically that the MTM provides similar performance to what is achieved with a finely specificity-optimized TM, by comparing their performance on both synthetic and real-world datasets.

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A solution to the classification problem with cellular automata

Cited by 10 publications

References 8 publications

Modeling Future Impacts on Land Cover of Rapid Expansion of Hazelnut Orchards: A Case Study on Samsun, Turkey

Modeling Future Impacts on Land Cover of Rapid Expansion of Hazelnut Orchards: A Case Study on Samsun, Turkey

CURIE: a cellular automaton for concept drift detection

A Tsetlin Machine with Multigranular Clauses

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