Heterogeneous ensemble selection for evolving data streams.

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model. In contrast, by combining several types of different learning models, a heterogeneous ensemble system can achieve greater diversity among its members, which helps to improve its performance. Although heterogeneous ensemble systems have achieved many successes in the batch classification setting, it is not trivial to extend them directly to the data stream setting. In this study, we propose a novel HEterogeneous Ensemble Selection (HEES) method, which dynamically selects an appropriate subset of base classifiers to predict data under the stream setting. We are inspired by the observation that a well-chosen subset of good base classifiers may outperform the whole ensemble system. Here, we define a good candidate as one that expresses not only high predictive performance but also high confidence in its prediction. Our selection process is thus divided into two sub-processes: accurate-candidate selection and confident-candidate selection. We define an accurate candidate in the stream context as a base classifier with high accuracy over the current concept, while a confident candidate as one with a confidence score higher than a certain threshold. In the first sub-process, we employ the prequential accuracy to estimate the performance of a base classifier at a specific time, while in the latter sub-process, we propose a new measure to quantify the predictive confidence and provide a method to learn the threshold incrementally. The final ensemble is formed by taking the intersection of the sets of confident classifiers and accurate classifiers. Experiments on a wide range of data streams show that the proposed method achieves competitive performance with lower running time in comparison to the state-of-the-art online ensemble methods

Selected Computing Research Papers Volume 2 June 2013

An Evaluation of Current Innovations for Solving Hard Disk Drive Vibration Problems (Isiaq Adeola) ........................................................................................................ 1 A Critical Evaluation of the Current User Interface Systems Used By the Blind and Visually Impaired (Amneet Ahluwalia) ................................................................................ 7 Current Research Aimed At Improving Bot Detection In Massive Multiplayer Online Games (Jamie Burnip) ........................................................................................................ 13 Evaluation Of Methods For Improving Network Security Against SIP Based DoS Attacks On VoIP Network Infrastructures (David Carney) ................................................ 21 An Evaluation of Current Database Encryption Security Research (Ohale Chidiebere) .... 29 A Critical Appreciation of Current SQL Injection Detection Methods (Lee David Glynn) .............................................................................................................. 37 An Analysis of Current Research into Music Piracy Prevention (Steven Hodgson) .......... 43 Real Time On-line Analytical Processing: Applicability Of Parallel Processing Techniques (Kushatha Kelebeng) ....................................................................................... 49 Evaluating Authentication And Authorisation Method Implementations To Create A More Secure System Within Cloud Computing Technologies (Josh Mallery) ................... 55 A Detailed Analysis Of Current Computing Research Aimed At Improving Facial Recognition Systems (Gary Adam Morrissey) ................................................................... 61 A Critical Analysis Of Current Research Into Stock Market Forecasting Using Artificial Neural Networks (Chris Olsen) ........................................................................... 69 Evaluation of User Authentication Schemes (Sukhdev Singh) .......................................... 77 An Evaluation of Biometric Security Methods for Use on Mobile Devices (Joe van de Bilt) .................................................................................................................. 8

Local quality-based matching of faces for watchlist screening applications

Video surveillance systems are often exploited by safety organizations for enhanced security and situational awareness. A key application in video surveillance is watchlist screening where target individuals are enrolled to a still-to-video Face Recognition (FR) system using single still images captured a priori under controlled conditions. Watchlist Screening is a very challenging application. Indeed, the latter must provide accurate decisions and timely recognition using limited number of reference faces for the system’s enrolment. This issue is often called the "Single Sample Per Person" (SSPP) problem. Added to that, uncontrolled factors such as variations in illumination pose and occlusion is unpreventable in real case video surveillance which causes the degradation of the FR system’s performance. Another major problem in such applications is the camera interoperability. This means that there is a huge gap between the camera used for taking the still images and the camera used for taking the video surveillance footage in terms of quality and resolution. This issue hinders the classification process then decreases the system‘s performance. Controlled and uniform lighting is indispensable for having good facial captures that contributes in the recognition performance of the system. However, in reality, facial captures are poor in illumination factor and are severely affecting the system’s performance. This is why it is important to implement a FR system which is invariant to illumination changes. The first part of this Thesis consists in investigating different illumination normalization (IN) techniques that are applied at the pre-processing level of the still-to-video FR. Afterwards IN techniques are compared to each other in order to pinpoint the most suitable technique for illumination invariance. In addition, patch-based methods for template matching extracts facial features from different regions which offers more discriminative information and deals with occlusion issues. Thus, local matching is applied for the still-to-video FR system. For that, a profound examination is needed on the manner of applying these IN techniques. Two different approaches were conducted: the global approach which consists in performing IN on the image then performs local matching and the local approach which consists in primarily dividing the images into non overlapping patches then perform on individually on each patch each IN technique. The results obtained after executing these experiments have shown that the Tan and Triggs (TT) and Multi ScaleWeberfaces are likely to offer better illumination invariance for the still-to-video FR system. In addition to that, these outperforming IN techniques applied locally on each patch have shown to improve the performance of the FR compared to the global approach. The performance of a FR system is good when the training data and the operation data are from the same distribution. Unfortunately, in still-to-video FR systems this is not satisfied. The training data are still, high quality, high resolution and frontal images. However, the testing data are video frames, low quality, low resolution and varying head pose images. Thus, the former and the latter do not have the same distribution. To address this domain shift, the second part of this Thesis consists in presenting a new technique of dynamic regional weighting exploiting unsupervised domain adaptation and contextual information based on quality. The main contribution consists in assigning dynamic weights that is specific to a camera domain.This study replaces the static and predefined manner of assigning weights. In order to assess the impact of applying local weights dynamically, results are compared to a baseline (no weights) and static weighting technique. This context based approach has proven to increase the system’s performance compared to the static weighting that is dependent on the dataset and the baseline technique which consists of having no weights. These experiments are conducted and validated using the ChokePoint Dataset. As for the performance of the still-to-video FR system, it is evaluated using performance measures, Receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve analysis

Adapting heterogeneous ensembles with particle swarm optimization for video face recognition

In video-based face recognition applications, matching is typically performed by comparing query samples against biometric models (i.e., an individual’s facial model) that is designed with reference samples captured during an enrollment process. Although statistical and neural pattern classifiers may represent a flexible solution to this kind of problem, their performance depends heavily on the availability of representative reference data. With operators involved in the data acquisition process, collection and analysis of reference data is often expensive and time consuming. However, although a limited amount of data is initially available during enrollment, new reference data may be acquired and labeled by an operator over time. Still, due to a limited control over changing operational conditions and personal physiology, classification systems used for video-based face recognition are confronted to complex and changing pattern recognition environments. This thesis concerns adaptive multiclassifier systems (AMCSs) for incremental learning of new data during enrollment and update of biometric models. To avoid knowledge (facial models) corruption over time, the proposed AMCS uses a supervised incremental learning strategy based on dynamic particle swarm optimization (DPSO) to evolve a swarm of fuzzy ARTMAP (FAM) neural networks in response to new data. As each particle in a FAM hyperparameter search space corresponds to a FAM network, the learning strategy adapts learning dynamics by co-optimizing all their parameters – hyperparameters, weights, and architecture – in order to maximize accuracy, while minimizing computational cost and memory resources. To achieve this, the relationship between the classification and optimization environments is studied and characterized, leading to these additional contributions. An initial version of this DPSO-based incremental learning strategy was applied to an adaptive classification system (ACS), where the accuracy of a single FAM neural network is maximized. It is shown that the original definition of a classification system capable of supervised incremental learning must be reconsidered in two ways. Not only must a classifier’s learning dynamics be adapted to maintain a high level of performance through time, but some previously acquired learning validation data must also be used during adaptation. It is empirically shown that adapting a FAM during incremental learning constitutes a type III dynamic optimization problem in the search space, where the local optima values and their corresponding position change in time. Results also illustrate the necessity of a long term memory (LTM) to store previously acquired data for unbiased validation and performance estimation. The DPSO-based incremental learning strategy was then modified to evolve the swarm (or pool) of FAM networks within an AMCS. A key element for the success of ensembles is tackled: classifier diversity. With several correlation and diversity indicators, it is shown that genoVIII type (i.e., hyperparameters) diversity in the optimization environment is correlated with classifier diversity in the classification environment. Following this result, properties of a DPSO algorithm that seeks to maintain genotype particle diversity to detect and follow local optima are exploited to generate and evolve diversified pools of FAMclassifiers. Furthermore, a greedy search algorithm is presented to perform an efficient ensemble selection based on accuracy and genotype diversity. This search algorithm allows for diversified ensembles without evaluating costly classifier diversity indicators, and selected ensembles also yield accuracy comparable to that of reference ensemble-based and batch learning techniques, with only a fraction of the resources. Finally, after studying the relationship between the classification environment and the search space, the objective space of the optimization environment is also considered. An aggregated dynamical niching particle swarm optimization (ADNPSO) algorithm is presented to guide the FAM networks according two objectives: FAM accuracy and computational cost. Instead of purely solving a multi-objective optimization problem to provide a Pareto-optimal front, the ADNPSO algorithm aims to generate pools of classifiers among which both genotype and phenotype (i.e., objectives) diversity are maximized. ADNPSO thus uses information in the search spaces to guide particles towards different local Pareto-optimal fronts in the objective space. A specialized archive is then used to categorize solutions according to FAMnetwork size and then capture locally non-dominated classifiers. These two components are then integrated to the AMCS through an ADNPSO-based incremental learning strategy. The AMCSs proposed in this thesis are promising since they create ensembles of classifiers designed with the ADNPSO-based incremental learning strategy and provide a high level of accuracy that is statistically comparable to that obtained through mono-objective optimization and reference batch learning techniques, and yet requires a fraction of the computational cost