Corrigendum: Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness

An author name was incorrectly spelled as \u201cUrszulaMarkowska-Kacznar.\u201d The correct spelling is \u201cUrszulaMarkowska-Kaczmar.\u201d The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated

An evolutionary algorithm determining a defuzzyfication functional

Order fuzzy numbers are defined that make it possible to deal with fuzzy inputs quantitatively, exactly in the same way as with real numbers, together with four algebraic operations. An approximation formula is given for a defuzzyfication functional that plays the main role when dealing with fuzzy controllers and fuzzy inference systems. A dedicated evolutionary algorithm is presented in order to determine the form of a functional when a training set is given. The form of a genotype composed of three types of chromosomes and the fitness function are given and Genetic operators are proposed

Interpreting Neural Networks Prediction for a Single Instance via Random Forest Feature Contributions

In this paper, we are focusing on the problem of interpreting Neural Networks on the instance level. The proposed approach uses the Feature Contributions, numerical values that domain experts further interpret to reveal some phenomena about a particular instance or model behaviour. In our method, Feature Contributions are calculated from the Random Forest model trained to mimic the Artificial Neural Network’s classification as close as possible. We assume that we can trust the Feature Contributions results when both predictions are the same, i.e., Neural Network and Feature Contributions give the same results. The results show that this highly depends on the level the Neural Network is trained because the error is then propagated to the Random Forest model. For good trained ANNs, we can trust in interpretation based on Feature Contributions on average in 80%

Evolutionary approach to rule extraction from medical data

In the paper the method called CGA based on a cooperating genetic algorithm is presented. The CGA is developed for searching a set of rules describing classes in classification problems on the basis of training examples. The details of the method, such as a schema of coding (a chromosome), and a fitness function are shortly described. The method is independent of the type of attributes and it allows choosing different evaluation functions. Developed method was tested using different benchmark data sets. Next, in order to evaluate the efficiency of CGA, it was tested using the Breast Cancer data set with 10 fold cross validation technique

Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications

A common problem in Data Mining (DM) is the presence of noise in the data being mined. Artificial neural networks (ANN) are robust and have a good tolerance to noise, which makes them suitable for mining very noisy data. Although they may achieve high classification accuracy, they have the well-known disadvantage of having black-box nature and not discovering any high-level rule that can be used as a support for human understanding. The main challenge in using ANN in DM applications is to get explicit knowledge from these models. For this purpose, a study on knowledge acquirement from trained ANNs for classification problems is presented. The proposed method uses Touring Ant Colony Optimization (TACO) algorithm for extracting accurate and comprehensible rules from databases via trained artificial neural networks. The suggested algorithm is experimentally evaluated on different benchmark data sets. Results show that the proposed approach has a potential to generate accurate and concise rules