Araken M. Santos

Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles

One of the most important steps in the design of a multi-classifier system (MCS), also known as ensemble, is the choice of the components (classifiers). This step is very important to the overall performance of a MCS since the combination of a set of identical classifiers will not outperform the individual members. The ideal situation would be a set of classifiers with uncorrelated errors - they would be combined in such a way as to minimize the effect of these failures. This paper presents an extensive evaluation of how the choice of the components (classifiers) can affect the performance of several combination methods (selection-based and fusion-based methods). An analysis of the diversity of the MCSs when varying their components is also performed. As a result of this analysis, it is aimed to help designers in the choice of the individual classifiers and combination methods of an ensemble.

Investigating the Use of an Agent-Based Multi-classifier System for Classification Tasks

This paper proposes NeurAge, an agent-based multi-classifier system for classification tasks. This system is composed of several neural classifiers (called neural agents) and its main aim is to overcome some drawbacks of multi-classifier systems and, as a consequence, to improve performance of such systems.

Ensembles of ARTMAP-based neural networks: an experimental study

ARTMAP-based models are neural networks which use a match-based learning procedure. The main advantage of ARTMAP-based models over error-based models, such as Multi-Layer Perceptron, is the learning time, which is considered as significantly fast. This feature is extremely important in complex systems that require the use of several models, such as ensembles or committees, since they produce robust and fast classifiers. Subsequently, some extensions of the ARTMAP model have been proposed, such as: ARTMAP-IC, RePART, among others. Aiming to add an extra contribution to ARTMAP context, this paper presents an analysis of ARTMAP-based models in ensemble systems. As a result of this analysis, two main goals are aimed, which are: to analyze the influence of the RePART model in ensemble systems and to detect any relation between diversity and accuracy in ensemble systems in order to use this relation in the design of these systems.

Performance and diversity evaluation in hybrid and non-hybrid structures of ensembles

This paper presents a wide evaluation of performance and diversity in hybrid and non-hybrid structures of ensembles. In applying some diversity measures at the chosen ensemble, it is intended to analyse the effect of varying diversity in ensembles and how the variation of diversity can affect the performance of several combination methods (selection-based and combination-based methods). Finally, it is also intended to understand the reasons that some combination methods are more affected by variation in diversity.

Using semi-supervised learning in multi-label classification problems

In traditional classification problems (single-label), patterns are associated with a single label from the set of disjoint labels. When an example can simultaneously belong to more than one label, we call it a multi-label classification problem. In relation to the learning strategy, the majority of classification methods requires a large number of training instances to be able to generalize the mapping function, making predictions with high accuracy. However, it is usually difficult to find a number of instances labeled which is sufficient to induce an accurate classification model. This problem is enhanced in the multi-label context, since the number of possible combinations in the label attributes increases considerably. In order to smooth out this problem, the idea of semi-supervised learning has emerged. It combines labeled and unlabeled data during the training phase. Some semi-supervised methods have been proposed for single-label classification methods. However, very little effort has been done in the context of multi-label classification. This paper proposes three semi-supervised methods for the multi-label classification. In order to validate the feasibility of these methods, an empirical analysis will be conducted, aiming to evaluate the performance of such methods in different tasks and using different evaluation metrics.

Applying semi-supervised learning in hierarchical multi-label classification

Abstract In classification problems with hierarchical structures of labels, the target function must assign labels that are hierarchically organized and it can be used either for single-label (one label per instance) or multi-label classification problems (more than one label per instance). In parallel to these developments, the idea of semi-supervised learning has emerged as a solution to the problems found in a standard supervised learning procedure (used in most classification algorithms). It combines labelled and unlabelled data during the training phase. Some semi-supervised methods have been proposed for single-label classification methods. However, very little effort has been done in the context of multi-label hierarchical classification. Therefore, this paper proposes a new method for supervised hierarchical multi-label classification, called HMC-RA k EL. Additionally, we propose the use of semi-supervised learning, self-training, in hierarchical multi-label classification, leading to three new methods, called HMC-SSBR, HMC-SSLP and HMC-SSRA k EL. In order to validate the feasibility of these methods, an empirical analysis will be conducted, comparing the proposed methods with their corresponding supervised versions. The main aim of this analysis is to observe whether the semi-supervised methods proposed in this paper have similar performance of the corresponding supervised versions.

Using confidence values in multi-label classification problems with semi-supervised learning

In most traditional classification methods, each instance is associated with one single nominal target variable (single-label problems). However, there are also cases where an instance can be associated with more than one label simultaneously, referring to as multi-label classification problems. One of the main problems with classification methods is that many of these require a high number of instances to be able to generalize the mapping function, making predictions with high accuracy. In order to smooth out this problem, the idea of semi-supervised learning has emerged. It combines labeled and unlabelled data during the training phase. However, in semi-supervised learning, it is important to define an efficient process of assignments of instances. This paper proposes three semi-supervised methods for the multilabel classification, focusing on the use of a confidence parameter in the process of automatic assignment of labels. In order to validate the feasibility of these methods, an empirical analysis will be conducted, aiming to evaluate the performance of such methods in different situations, besides the use of different evaluation metrics on this performance.

Simulating working environments through the use of personality-based agents

This paper presents a multi-agent simulation system, named SimOrg (Simulation of human organization), in which the personality aspect is incorporated in the internal functioning of the agents. The general architecture of the agents has been based on the theory of human personality from Theodore Millon [13]. The presented simulation has been used to simulate working environments, focusing on the training process within an organization. The main aim of this paper is to analyze how personality can affect the performance of the agent when simulating working environments.

A personality-based model of agents for representing individuals in working organizations

This paper proposes an agent architecture which can be used to represent individuals within a working organization. The proposed architecture has been based on the theory of human personality and its working relationship from Theodore Milton. The main aim of this paper is to describe a suitable representation of individual behaviors which is able to be mapped to collective patterns of a human organization. The proposed architecture has been used in the SimOrg project, which aims to apply a multi-agent simulation in human organizations.

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.