Antonino Feitosa Neto

Classifier ensembles and optimization techniques to improve the performance of cancellable fingerprint

The main aim of biometric-based identification systems is to automatically recognize individuals based on their physiological and/or behavioural characteristics such as fingerprint, face, hand-geometry, among others. These systems offer several advantages over traditional forms of identity protection. However, there are still some important aspects that need to be addressed in these systems. The main questions are concerned with the security of biometric authentication systems since it is important to ensure the integrity and public acceptance of these systems. In order to avoid the problems arising from compromised biometric templates, the concept of cancellable biometrics has recently been introduced. The concept is to transform a biometric trait into a new representation for enrolment and matching. Although cancellable biometrics were proposed to solve privacy concerns, the concept raises new issues, since they make the authentication problem more complex and difficult to solve. Thus, more effective authentication structures are needed to perform these tasks. In this paper, we investigate the use of ensemble systems in cancellable biometrics, using fingerprint-based identification to illustrate the possible benefits accruing. In order to increase the effectiveness of the proposed ensemble systems, three feature selection methods will be used to distribute the attributes among the individual classifiers of an ensemble. The main aim of this paper is to analyse the performance of such well-established structures on transformed biometric data to determine whether they have a positive effect on the performance of this complex and difficult task.

Optimization techniques for the selection of members and attributes in ensemble systems

Although ensemble systems have been proved to be efficient for pattern recognition tasks, its elaboration and design is not an easy task. Some aspects such as the choice of its individual classifiers and the use of feature selection methods are very difficult to define. In addition, these aspects can have a strong effect in the accuracy of these systems, leading, for instance, to cases where the produced ensembles have no performance improvement. In order to avoid this situation, there is a great deal of research to select individual classifiers or distribute attributes to the individual classifiers of ensemble systems. In most of these works, however, only one aspect is tackled (either member selection or feature selection). In this paper, we present an analysis of two well-known optimization techniques to choose the ensemble members and to select attributes for these individual classifiers. In order to do this analysis, we use accuracy as well as two recently proposed diversity measures as parameters, in a multi-objective optimization problem.

Enhancing Performance of Cancellable Fingerprint Biometrics Using Classifier Ensembles

Biometric systems automatically recognize individuals based on their physiological and/or behavioral characteristics like fingerprint, face, hand-geometry, iris, retina, palmprint, voice, gait, signature, and keystroke dynamics. These systems offer several advantages over traditional forms of identity protection (e.g. password-based). Nevertheless, many biometric characteristics are immutable, resulting in permanent compromise when a template is stolen. In order to replace compromised biometric templates, the concept of cancellable biometrics has recently been introduced. The concept is to transform a biometric trait into a new one for enrollment and matching. Although cancellable biometrics were proposed to solve privacy concerns, the concept raises new issues, since they make the authentication problem more complex and difficult to solve. Thus, more effective authentication structures are needed to perform these tasks. In this paper, we investigate the use of ensemble systems in cancellable biometrics, using fingerprint-based identification to illustrate the possible benefits accruing. In order to perform this analysis, we have proposed a simpler version of a transformation function used to create cancellable fingerprint. The main aim of this paper is to analyze the performance of such well-established structures on transformed biometric data to determine whether they have a positive effect on the performance of this complex and difficult task.

Using a reinforcement-based feature selection method in Classifier Ensemble

In the design of Classifier Ensembles, diversity is considered as one of the main aspects to be taken into account, since there is no gain in combining identical classification methods. One way of increasing diversity is to use feature selection methods in order to select subsets of attributes for the individual classifiers. In this paper, it is investigated the use of a simple reinforcement-based method, called ReinSel, in ensemble systems. More specifically, it is aimed to evaluate the capability of this method to select the correct attributes of a dataset, avoiding unimportant and noisy attributes.

Evaluating classification methods applied to multi-label tasks in different domains

In traditional classification problems (single-label), patterns are associated with a single label from the set of disjoint labels (classes). When an example can simultaneously belong to more than one label, this classification problem is known as multi-label classification problem. Multi-label classification methods have been increasingly used in modern application, such as music categorization, functional genomics and semantic annotation of images. This paper presents a comparative analysis of some existing multi-label classification methods applied to different domains. The main aim of this analysis is to evaluate the performance of such methods in different tasks and using different evaluation metrics.

Static and Dynamic Weights in Ensemble Systems Built by Class-Based Feature Selection Methods

The use of feature selection methods in ensemble systems has been shown to be efficient, since it reduces the dimensionality while increases the diversity among the individual classifiers of these systems. The ReinSel method, a simple reinforcement-based process, for instance, has been proposed to select feature for the individual classifiers of an ensemble system. This method distributes the attributes through the use of a class-based process (using One-Against-All, OAA, classifiers). In this paper, we investigate the use of weights in order to enhance the efficiency of the ensemble systems created by class-based feature selection methods. These weights will not be used in feature selection methods, but in the ensemble systems created as the result of these methods. More specifically, four different types of weights will be used in this investigation, in which three of them are defined before the testing phase and became unchanged during the testing phase (static). The last one uses a knn-based method to define the weights for each testing pattern (dynamic).

An exploratory study of mono and multi-objective metaheuristics to ensemble of classifiers

This paper performs an exploratory study of the use of metaheuristic optimization techniques to select important parameters (features and members) in the design of ensemble of classifiers. In order to do this, an empirical investigation, using 10 different optimization techniques applied to 23 classification problems, will be performed. Furthermore, we will analyze the performance of both mono and multi-objective versions of these techniques, using all different combinations of three objectives, classification error as well as two important diversity measures to ensembles, which are good and bad diversity measures. Additionally, the optimization techniques will also have to select members for heterogeneous ensembles, using k-NN, Decision Tree and Naive Bayes as individual classifiers and they are all combined using the majority vote technique. The main aim of this study is to define which optimization techniques obtained the best results in the context of mono and multi-objective as well as to provide a comparison with classical ensemble techniques, such as bagging, boosting and random forest. Our findings indicated that three optimization techniques, Memetic, SA and PSO, provided better performance than the other optimization techniques as well as traditional ensemble generator (bagging, boosting and random forest).

Multiobjective Optimization Techniques for Selecting Important Metrics in the Design of Ensemble Systems

Ensemble systems are classification structures that apply a two‐level decision‐making process, in which the first level produces the outputs of the individual classifiers and the second level produces the output of the combination method (final output). Although ensemble systems have been proven to be efficient for pattern recognition tasks, its efficient design is not an easy task. This article investigates the influence of two diversity measures when used explicitly to guide the design of ensemble systems. These diversity measures were proposed recently, and they proved to be very interesting for the diversity–accuracy dilemma. To perform this investigation, we will use two well‐known optimization techniques, genetic algorithms, and tabu search, in their mono‐objective and multiobjective versions. As objectives of the optimization techniques, we use error rate and two diversity measures as well as all possible combinations of these three objectives. In this article, we aim to analyze which set of objectives can generate more accurate ensembles. In addition, we aim to analyze whether or not the diversity measures (good and bad diversities) have a positive effect in the design of ensemble systems, mainly if they can replace the error rate as an optimization objective without incurring significant losses in the accuracy level of the generated ensembles.

Ensemble-based methods for cancellable biometrics

In this paper, we investigate the use of genetic algorithms and ensemble systems in cancellable biometrics, using fingerprint-based identification to illustrate the possible benefits accruing. The main aim is to analyze the performance of these well-established structures on transformed biometric data to determine whether they have a positive effect on the performance of this complex and difficult task.

Population-based bio-inspired algorithms for cluster ensembles optimization

Clustering algorithms have been applied to different problems in many different real-word applications. Nevertheless, each algorithm has its own advantages and drawbacks, which can result in different solutions for the same problem. Therefore, the combination of different clustering algorithms (cluster ensembles) has emerged as an attempt to overcome the limitations of each clustering technique. The use of cluster ensembles aims to combine multiple partitions generated by different clustering algorithms into a single clustering solution (consensus partition). Recently, several approaches have been proposed in the literature in order to optimize or to improve continuously the solutions found by the cluster ensembles. As a contribution to this important subject, this paper presents an investigation of five bio-inspired techniques in the optimization of cluster ensembles (Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Coral Reefs Optimization and Bee Colony Optimization). In this investigation, unlike most of the existing work, an evaluation methodology for assessing three important aspects of cluster ensembles will be presented, assessing robustness, novelty and stability of the consensus partition delivered by different optimization algorithms. In order to evaluate the feasibility of the analyzed techniques, an empirical analysis will be conducted using 20 different problems and applying two different indexes in order to examine its efficiency and feasibility. Our findings indicated that the best population-based optimization method was PSO, followed by CRO, AG, BCO and ACO, for providing robust and stable consensus partitions.