Search

In this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets. [ABSTRACT FROM AUTHOR]

Training neural networks in distinguishing different emotions from physiological signals frequently involves fuzzy definitions of each affective state. In addition, manual design of classification tasks often uses sub-optimum classifier parameter settings, leading to average classification performance. In this study, an attempt to create a framework for multi-layered optimization of an ensemble of classifiers to maximize the system's ability to learn and classify affect, and to minimize human involvement in setting optimum parameters for the classification system is proposed. Using fuzzy adaptive resonance theory mapping (ARTMAP) as the classifier template, genetic algorithms (GAs) were employed to perform exhaustive search for the best combination of parameter settings for individual classifier performance. Speciation was implemented using subset selection of classification data attributes, as well as using an island model genetic algorithms method. Subsequently, the generated population of optimum classifier configurations was used as candidates to form an ensemble of classifiers. Another set of GAs were used to search for the combination of classifiers that would result in the best classification ensemble accuracy. The proposed methodology was tested using two affective data sets and was able to produce relatively small ensembles of fuzzy ARTMAPs with excellent affect recognition accuracy. [ABSTRACT FROM AUTHOR]

Highlights: [•] An ensemble of hybrid intelligent models is applied to condition monitoring tasks. [•] A review on condition monitoring of induction motors is presented. [•] The proposed model is able to learn incrementally and explain its predictions. [•] The results ascertain the usefulness of the model for condition monitoring tasks. [•] The knowledge base is presented as a decision tree for interpretation by users. [ABSTRACT FROM AUTHOR]

Highlights: [•] A hybrid intelligent system is proposed for medical data classification tasks. [•] The proposed system is able to learn incrementally and explain its predictions. [•] Benchmark medical data sets are used to evaluate the effectiveness of the system. [•] The results ascertain the usefulness of the system for medical decision support. [•] The knowledge base is presented as a decision tree for interpretation by users. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-step prediction model for time series prediction, i.e. Meta-cognitive Recurrent Kernel Online Sequential Extreme Learning Machine with Drift Detector Mechanism (Meta-RKOS-ELM ALD). Recurrent multi-step algorithm is applied to release the limitation in the number of prediction steps, and Drift Detector Mechanism (DDM) is used to overcome the problem of concept drift in the prediction model. The new meta-cognitive strategy decides the way of the incoming data during training, which decreases the training computation of prediction model and solves the parameter dependency. In our evaluation, we use a total of six artificial data sets and three real-world data sets (Standard & Poor's 500 Index, Shanghai Stock Exchange Composite Index, and Ozone Concentration in Toronto) to prove the ability of kernel filters, the detecting ability of concept drift detector, and situation of applying meta-cognitive strategy in our proposed model. Experiments results indicate that the Meta-KOS-ELM ALD with DDM has better forecasting ability in various predicting periods with the shortest learning time, as compared with other algorithms. [ABSTRACT FROM AUTHOR]