Wei Shiung Liew

Probabilistic ensemble Fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms

Abstract In this study, a comprehensive methodology for overcoming the design problem of the Fuzzy ARTMAP neural network is proposed. The issues addressed are the sequence of training data for supervised learning and optimum parameter tuning for parameters such as baseline vigilance. A genetic algorithm search heuristic was chosen to solve this multi-objective optimization problem. To further augment the ARTMAP’s pattern classification ability, multiple ARTMAPs were optimized via genetic algorithm and assembled into a classifier ensemble. An optimal ensemble was realized by the inter-classifier diversity of its constituents. This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. The best-performing classifiers were then combined in an ensemble, using probabilistic voting for decision combination. This study also integrated the disparate methods to operate within a single framework, which is the proposed novel method for creating an optimum classifier ensemble configuration with minimum user intervention. The methodology was benchmarked using popular data sets from UCI machine learning repository.

Affect classification using genetic-optimized ensembles of fuzzy ARTMAPs

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 systems 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.

Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

Medical data classification problems with two real data sets are investigated.A literature review on biomedical signal processing techniques is provided.The data sets are corrupted with noise to assess the robustness of different models.The logistic regression model produces the best results in noise-free environments.Ensemble-based learning model yields the best results in noisy environments. 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.

PSO optimization of synergetic neural classifier for multichannel emotion recognition

In the world of technology, human-machine interaction is becoming more common and will perhaps be a part of our life in the future. Human-machine interaction is more natural if machines are able to perceive and respond to human non-verbal communication such as emotions instead of relying only on audio-visual emotion channels. A particle swarm optimization (PSO) of synergetic neural classifier for multimodal emotion recognition is proposed in this paper. In the experiments, a music induction method which elicits natural emotional reactions from the subject is used and four-channel biosensors are used to obtain electromyogram (EMG), electrocardiogram (ECG), skin conductivity (SC) and respiration changes (RSP) of the subject. The most significant features are extracted via testing several feature selection/reduction methods. Four classes of emotions, that is, joy, anger, sadness, and pleasure are considered and the synergetic neural classifier is used for multimodal emotion recognition. Weights are assigned to the different channels of the classifier and PSO is applied to optimize the weights for enhancing performance. Fast classification speed has been achieved and the experimental results look promising.

Genetic-optimized classifier ensemble for cortisol salivary measurement mapping to electrocardiogram features for stress evaluation

This work presents our findings to map salivary cortisol measurements to electrocardiogram (ECG) features to create a physiological stress identification system. An experiment modelled on the Trier Social Stress Test (TSST) was used to simulate stress and control conditions, whereby salivary measurements and ECG measurements were obtained from student volunteers. The salivary measurements of stress biomarkers were used as objective stress measures to assign a three-class labelling (Low-Medium-High stress) to the extracted ECG features. The labelled features were then used for training and classification using a genetic-ordered ARTMAP with probabilistic voting for analysis on the efficacy of the ECG features used for physiological stress recognition. The ECG features include time-domain features of the heart rate variability and the ECG signal, and frequency-domain analysis of specific frequency bands related to the autonomic nervous activity. The resulting classification method scored approximately 60-69% success rate for predicting the three stress classes.

Classifying Stress From Heart Rate Variability Using Salivary Biomarkers as Reference

An accurate and noninvasive stress assessment from human physiology is a strenuous task. In this paper, a pattern recognition system to learn complex correlates between heart rate variability (HRV) features and salivary stress biomarkers is proposed. Using the Trier social stress test, heart rate and salivary measurements were obtained from volunteers under varying levels of stress induction. Measurements of salivary alpha-amylase and cortisol were used as objective measures of stress, and were correlated with the HRV features using fuzzy ARTMAP (FAM). In improving the predictive ability of the ARTMAPs, techniques, such as genetic algorithms for parameter optimization and voting ensembles, were employed. The ensemble of FAMs can be used for predicting stress responses of salivary alpha-amylase or cortisol using heart rate measurements as the input. Using alpha-amylase as the stress indicator, the ensemble was able to classify stress from heart rate features with 75% accuracy, and 80% accuracy when cortisol was used.

Classification of transcranial Doppler signals using individual and ensemble recurrent neural networks

Transcranial Doppler (TCD) is a reliable technique with the advantage of being non-invasive for the diagnosis of cerebrovascular diseases using blood flow velocity measurements pertaining to the cerebral arterial segments. In this study, the recurrent neural network (RNN) is used to classify TCD signals captured from the brain. A total of 35 real, anonymous patient records are collected, and a series of experiments for stenosis diagnosis is conducted. The extracted features from the TCD signals are used for classification using a number of RNN models with recurrent feedbacks. In addition to individual RNN results, an ensemble RNN model is formed in which the majority voting method is used to combine the individual RNN predictions into an integrated prediction. The results, which include the accuracy, sensitivity, and specificity rates as well as the area under the Receiver Operating Characteristic curve, are compared with those from the Random Forest Ensemble model. The outcome positively indicates the usefulness of the RNN ensemble as an effective method for detecting and classifying blood flow velocity changes due to brain diseases.

Genetic Optimized Fuzzy Extreme Learning Machine Ensembles for Affect Classification

This paper presents a method for generating an optimized ensemble of fuzzy extreme learning machines (FELM) using a combination of genetic algorithms with a Bayesian Information Criterion (GA-BIC) fitness function. The operation of the FELM is equivalent to that of a fuzzy inference system, and is used for learning and classifying a given data set. The relative simplicity of the FELM structure enables a large number of FELMs to be generated in a short time. GA-BIC is used to select the minimum number of FELMs while maximizing the effectiveness of the classifier ensemble.

Hierarchical Parallel Genetic Optimization Fuzzy ARTMAP Ensemble

In this paper, a framework for designing optimum pattern classifiers is proposed. The fuzzy ARTMAP (FAM) is first used as a base classifier. Multiple FAM classifiers form an ensemble to improve classification accuracy. Multi-objective genetic algorithms (GAs) are then used to search for the best combinations of variables, for the FAM classifiers. Based on the population of potential solutions, another GA selects the best combination of FAM classifiers to create an ensemble. Individual decisions are combined using a probabilistic voting scheme. To increase the inter-classifier diversity, a hierarchical parallel GA variant and a negative correlation method is employed during the genetic optimization phase for the ensemble evaluation. The proposed framework is evaluated using benchmark and real-world data sets, and the results compared with literature. Results positively indicate the proposed framework is effective in undertaking data classification tasks.

Optimizing fuzzy ARTMAP ensembles using hierarchical parallel genetic algorithms and negative correlation

This study demonstrates a system and methods for optimizing a pattern classification task. A genetic algorithm method was employed to optimize a Fuzzy ARTMAP pattern classification task, followed by another genetic algorithm to assemble an ensemble of classifiers. Two parallel tracks were performed in order to assess a diversity-enhanced classifier and ensemble optimization methodology in comparison with a more straightforward method that does not rely on diverse classifiers and ensembles. Ensembles designed with diverse classifiers outperformed diversity-neutral classifiers in 62.50% of the tested cases. Using a negative correlation method to manipulate inter-classifier diversity, diverse ensembles performed better than non-diverse ensembles in 81.25% of the tested cases.