Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation

Kaburlasos, Vassilis G.; Athanasiadis, Ioannis N.; Mitkas, Pericles A.

doi:10.1016/j.ijar.2006.08.001

Cited by 110 publications

(61 citation statements)

References 85 publications

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“…In order to establish a fair performance comparison, results with the following classification models were examined in the same context and under the same experimental conditions: multilayer perceptrons (MLP) [1,2], morphological-rank-linear neural network (MRLNN) [35], morphological perceptron with competitive learning (MP/CL) [9], single layer morphological perceptron (SLMP) [36], fuzzy lattice neural network (FLNN) [13], fuzzy lattice reasoning (FLR) [37], k-nearest neighbors (KNN) [38], decision tree (DT) [39,40], support vector machine (SVM) [2] and dilation-erosion-linear perceptron with gradient-based learning, that is, the DELP(BP) [27].…”

Section: Simulations and Experimental Resultsmentioning

confidence: 99%

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A Swarm-based Evolutionary Morphological Approach for Binary Classification Problems

Araújo¹,

Oliveira²,

Meira³

2013

L&NLM

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-In this work we propose a swarm-based evolutionary morphological approach to deal with binary classification problems. It consists of a hybrid neuron based on principles of mathematical morphology and lattice theory, referred to as dilation-erosion-linear perceptron (DELP). We also present a swarm-based evolutionary learning process, called DELP(PSO), using a particle swarm optimizer (PSO) to design the DELP model, due to some drawbacks from gradient estimation of morphological operators in the classical learning process of the DELP. Besides, we conduct an experimental analysis using two relevant binary classification problems and the obtained results are discussed and compared with those obtained by established techniques in the literature.

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Section: Simulations and Experimental Resultsmentioning

confidence: 99%

“…For the FLNN model we used the same design process and parameters definition suggested by [9,13]. For the FLR model we used the same design process and parameters definition suggested by [9,37]. For the KNN model we used the 10-fold cross-validation to determine the best value of k (1,2,. .…”

Section: Simulations and Experimental Resultsmentioning

confidence: 99%

A Swarm-based Evolutionary Morphological Approach for Binary Classification Problems

Araújo¹,

Oliveira²,

Meira³

2013

L&NLM

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“…Training data are clustered into fuzzy lattices, each one of which is assigned to a certain class, based on a criterion called inclusion measure. In this way, the extracted decision model consists of a set of fuzzy lattice rules (Kaburlasos et al, 2006). b k-Profile is a new algorithm that attempts to determine agent dynamic traversal rules, by dealing with their behaviors as states.…”

Section: Training and Retraining With The Data Minermentioning

confidence: 99%

Data mining for agent reasoning: A synergy for training intelligent agents

Symeonidis

Chatzidimitriou

Athanasiadis

et al. 2007

Engineering Applications of Artificial Intelligence

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The task-oriented nature of data mining (DM) has already been dealt successfully with the employment of intelligent agent systems that distribute tasks, collaborate and synchronize in order to reach their ultimate goal, the extraction of knowledge. A number of sophisticated multi-agent systems (MAS) that perform DM have been developed, proving that agent technology can indeed be used in order to solve DM problems. Looking into the opposite direction though, knowledge extracted through DM has not yet been exploited on MASs. The inductive nature of DM imposes logic limitations and hinders the application of the extracted knowledge on such kind of deductive systems. This problem can be overcome, however, when certain conditions are satisfied a priori. In this paper, we present an approach that takes the relevant limitations and considerations into account and provides a gateway on the way DM techniques can be employed in order to augment agent intelligence. This work demonstrates how the extracted knowledge can be used for the formulation initially, and the improvement, in the long run, of agent reasoning. r

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“…Of the three research areas mentioned, order theory undoubtedly is the youngest. In recent years, as order and partial ordered set theory were widely applied in the combinatorics [1,9,13,37,43], fuzzy mathematics [7,32,40,42,44], computer science [2,39], and even in the social science [14,15] etc.…”

Section: Introductionmentioning

confidence: 99%

A new view of relationship between atomic posets and complete (algebraic) lattices

2017

Open Mathematics

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Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation

Cited by 110 publications

References 85 publications

A Swarm-based Evolutionary Morphological Approach for Binary Classification Problems

A Swarm-based Evolutionary Morphological Approach for Binary Classification Problems

Data mining for agent reasoning: A synergy for training intelligent agents

A new view of relationship between atomic posets and complete (algebraic) lattices

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