Scalable approximate FRNN-OWA classification

Fuzzy Rough Nearest Neighbour classification with Ordered Weighted Averaging operators (FRNN-OWA) is an algorithm that classifies unseen instances according to their membership in the fuzzy upper and lower approximations of the decision classes. Previous research has shown that the use of OWA operators increases the robustness of this model. However, calculating membership in an approximation requires a nearest neighbour search. In practice, the query time complexity of exact nearest neighbour search algorithms in more than a handful of dimensions is near-linear, which limits the scalability of FRNN-OWA. Therefore, we propose approximate FRNN-OWA, a modified model that calculates upper and lower approximations of decision classes using the approximate nearest neighbours returned by Hierarchical Navigable Small Worlds (HNSW), a recent approximative nearest neighbour search algorithm with logarithmic query time complexity at constant near-100% accuracy. We demonstrate that approximate FRNN-OWA is sufficiently robust to match the classification accuracy of exact FRNN-OWA while scaling much more efficiently. We test four parameter configurations of HNSW, and evaluate their performance by measuring classification accuracy and construction and query times for samples of various sizes from three large datasets. We find that with two of the parameter configurations, approximate FRNN-OWA achieves near-identical accuracy to exact FRNN-OWA for most sample sizes within query times that are up to several orders of magnitude faster

Comparing Features Extraction Methods for Person Authentication Using EEG Signals

This chapter presents a comparison and analysis of six feature extraction methods which were often cited in the literature, namely wavelet packet decomposition (WPD), Hjorth parameter, mean, coherence, cross-correlation and mutual information for the purpose of person authentication using EEG signals. The experimental dataset consists of a selection of 5 lateral and 5 midline EEG channels extracted from the raw data published in UCI repository. The experiments were designed to assess the capability of the feature extraction methods in authenticating different users. Besides, the correlation-based feature selection (CFS) method was also proposed to identify the significant feature subset and enhance the authentication performance of the features vector. The performance measurement was based on the accuracy and area under ROC curve (AUC) values using the fuzzy-rough nearest neighbour (FRNN) classifier proposed previously in our earlier work. The results show that all the six feature extraction methods are promising. However, WPD will induce large vector set when the selected EEG channels increases. Thus, the feature selection process is important to reduce the features set before combining the significant features with the other small feature vectors set

EEG-Based Person Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique

High level security has nurtured the arisen of Electroencephalograms (EEG) signals as a noteworthy biometrics modality for person authentication modelling. Modelling distinctive characteristics among individuals, especially in a dynamic environment involves incremental knowledge updates from time to time. K-Nearest Neighbour (KNN) is a well-known incremental learning method which applies First-In-First-Out (FIFO) knowledge update strategy. However, it is not suitable for person authentication modelling because it cannot preserve the representative EEG signals patterns when individual characteristics changes over time. Fuzzy-Rough Nearest Neighbours (FRNN) technique is an outstanding technique to model uncertainty under an imperfect data condition. The current implementation of FRNN technique is not designed for incremental learning problem because there is no update function to incrementally reshape and reform the existing knowledge granules. Thus, this research aims to design an Incremental FRNN (IncFRNN) technique for person authentication modelling using feature extracted EEG signals from VEP electrodes. The IncFRNN algorithm updates the training set by employing a heuristic update method to maintain representative objects and eliminate rarely used objects. The IncFRNN algorithm is able to control the size of training pool using predefined window size threshold. EEG signals such as visual evoked potential (VEP) is unique but highly uncertain and difficult to process.There exists no consistant agreement on suitable feature extraction methods and VEP electrodes in the past literature. The experimental comparison in this research has suggested eight significant electrodes set located at the occipital area. Similarly, six feature extraction methods, i.e. Wavelet Packet Decomposition (WPD), mean of amplitude, coherence, crosscorrelation, hjorth parameter and mutual information were used construct the proposed person authentication model. The correlation-based feature selection (CFS) method was used to select representative WPD vector subset to eliminate redundancy before combining with other features. The electrodes, feature extraction, and feature selection analysis were tested using the benchmarking dataset from UCI repositories. The IncFRNN technique was evaluated using a collected EEG data from 37 subjects. The recorded datasets were designed in three different conditions of ambient noise influence to evaluate the performance of the proposed solution. The proposed IncFRNN technique was compared with its predecessor, the FRNN and IBk technique. Accuracy and area under ROC curve (AUC) were used to measure the authentication performance. The IncFRNN technique has achieved promising results. The results have been further validated and proven significant statistically using paired sample ttest and Wilcoxon sign-ranked test. The heuristic incremental update is able to preserve the core set of individual biometrics characteristics through representative EEG signals patterns in person authentication modelling. Future work should focus on the noise management in data acquisition and modelling process to improve the robustness of the proposed person authentication model

Fuzzy-Rough Nearest Neighbour Classification and Prediction

AbstractNearest neighbour (NN) approaches are inspired by the way humans make decisions, comparing a test object to previously encountered samples. In this paper, we propose an NN algorithm that uses the lower and upper approximations from fuzzy-rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other NN approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with leading classification and prediction methods. Moreover, we show that the robustness of our methods against noise can be enhanced effectively by invoking the approximations of the Vaguely Quantified Rough Set (VQRS) model, which emulates the linguistic quantifiers “some” and “most” from natural language

A scalable approach to fuzzy rough nearest neighbour classification with ordered weighted averaging operators

Fuzzy rough sets have been successfully applied in classification tasks, in particular in combination with OWA operators. There has been a lot of research into adapting algorithms for use with Big Data through parallelisation, but no concrete strategy exists to design a Big Data fuzzy rough sets based classifier. Existing Big Data approaches use fuzzy rough sets for feature and prototype selection, and have often not involved very large datasets. We fill this gap by presenting the first Big Data extension of an algorithm that uses fuzzy rough sets directly to classify test instances, a distributed implementation of FRNN-OWA in Apache Spark. Through a series of systematic tests involving generated datasets, we demonstrate that it can achieve a speedup effectively equal to the number of computing cores used, meaning that it can scale to arbitrarily large datasets

Fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets

We present fuzzy-rough-learn, the first Python library of fuzzy rough set machine learning algorithms. It contains three algorithms previously implemented in R and Java, as well as two new algorithms from the recent literature. We briefly discuss the use cases of fuzzy-rough-learn and the design philosophy guiding its development, before providing an overview of the included algorithms and their parameters