Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

Horizontes 2030: la igualdad en el centro del desarrollo sostenible. Síntesis

El mundo vive un cambio de época. La comunidad internacional, respondiendo a los desequilibrios económicos, distributivos y ambientales del estilo de desarrollo dominante, ha aprobado recientemente la Agenda 2030 para el Desarrollo Sostenible y sus 17 Objetivos. En este documento, que la Comisión Económica para América Latina y el Caribe (CEPAL) presenta a los Estados miembros en su trigésimo sexto período de sesiones, se complementa analíticamente esa Agenda sobre la base de la perspectiva estructuralista del desarrollo y desde el punto de vista de los países de América Latina y el Caribe. Sus propuestas se centran en la necesidad de impulsar un cambio estructural progresivo que aumente la incorporación de conocimiento en la producción, garantice la inclusión social y combata los efectos negativos del cambio climático. El foco de las reflexiones y propuestas para avanzar hacia un nuevo estilo de desarrollo radica en el impulso a la igualdad y la sostenibilidad ambiental. La creación de bienes públicos globales y de sus correlatos a nivel regional y de políticas nacionales es el núcleo desde el que se expande la visión estructuralista hacia un keynesianismo global y una estrategia de desarrollo centrada en un gran impulso ambiental.

Horizons 2030: Equality at the centre of sustainable development. Summary

The world is living a change of era. The 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals represent the international community’s response to the economic, distributive and environmental imbalances built up under the prevailing development pattern. This document, presented by the Economic Commission for Latin America and the Caribbean (ECLAC) to its member States at its thirty-sixth session, provides an analytical complement to the 2030 Agenda from a structuralist perspective and from the point of view of the Latin American and Caribbean countries. The proposals made here stem from the need to achieve progressive structural change in order to incorporate more knowledge into production, ensure social inclusion and combat the negative impacts of climate change. The reflections and proposals for advancing towards a new development pattern are geared to achieving equality and environmental sustainability. In these proposals, the creation of global and regional public goods and the corresponding domestic policies form the core for expanding the structuralist tradition towards a global Keynesianism and a development strategy centred around an environmental big push.

Horizontes 2030: a igualdade no centro do desenvolvimento sustentável. Síntese

O mundo vive uma mudança de época. A comunidade internacional, respondendo aos desequilíbrios econômicos, distributivos e ambientais do estilo de desenvolvimento dominante, aprovou recentemente a Agenda 2030 para o Desenvolvimento Sustentável e seus 17 Objetivos. Este documento, que a Comissão Econômica para a América Latina e o Caribe (CEPAL) apresenta aos Estados membros no trigésimo sexto período de sessões, complementa analiticamente essa Agenda com base na perspectiva estruturalista do desenvolvimento e sob o ponto de vista dos países da América Latina e do Caribe. Suas propostas se concentram na necessidade de impulsionar uma mudança estrutural progressiva que aumente a incorporação de conhecimento na produção, garanta a inclusão social e combata os efeitos negativos da mudança climática. As reflexões e propostas para avançar rumo a um novo estilo de desenvolvimento mantêm seu foco no impulso à igualdade e à sustentabilidade ambiental. A criação de bens públicos globais e de seus correlatos no âmbito regional e de políticas nacionais é o núcleo a partir do qual se expande a visão estruturalista para um keynesianismo global e uma estratégia de desenvolvimento concentrada num grande impulso ambiental.

Horizons 2030: l’égalité au coeur du développement durable. Synthèse

Le monde traverse un changement d’époque. Face aux déséquilibres économiques, distributifs et environnementaux du mode de développement prédominant, la communauté internationale vient d’adopter le Programme de développement durable à l’horizon 2030 et ses 17 objectifs. Le document que la Commission économique pour l’Amérique latine et les Caraïbes (CEPALC) présente aux états membres à l’occasion de sa trente-sixième session a pour but d’apporter un complément analytique à ce Programme dans la perspective structuraliste du développement et de l’optique des pays d’Amérique latine et des Caraïbes. Les propositions contenues dans ce document sont centrées sur la nécessité de promouvoir un changement structurel progressif qui favorise l’incorporation du savoir à la production, garantisse l’inclusion sociale et combatte les effets néfastes du changement climatique. Les réflexions et les propositions visant à avancer sur la voie d’un nouveau mode de développement sont axées sur la promotion de l’égalité et de la pérennité de l’environnement. La création de biens publics mondiaux et leurs équivalents à l’échelle régionale, ainsi que l’application de politiques nationales sont au coeur d’une vision structuraliste qui est appelée à évoluer vers un keynésianisme mondial et une stratégie de développement centrée sur un élan majeur pour l’environnement.

Horizontes 2030: la igualdad en el centro del desarrollo sostenible

El mundo vive un cambio de época. La comunidad internacional, respondiendo a los desequilibrios económicos, distributivos y ambientales del estilo de desarrollo dominante, ha aprobado recientemente la Agenda 2030 para el Desarrollo Sostenible y sus 17 Objetivos. En este documento, que la Comisión Económica para América Latina y el Caribe (CEPAL) presenta a los Estados miembros en su trigésimo sexto período de sesiones, se complementa analíticamente esa Agenda sobre la base de la perspectiva estructuralista del desarrollo y desde el punto de vista de los países de América Latina y el Caribe. Sus propuestas se centran en la necesidad de impulsar un cambio estructural progresivo que aumente la incorporación de conocimiento en la producción, garantice la inclusión social y combata los efectos negativos del cambio climático. El foco de las reflexiones y propuestas para avanzar hacia un nuevo estilo de desarrollo radica en el impulso a la igualdad y la sostenibilidad ambiental. La creación de bienes públicos globales y de sus correlatos a nivel regional y de políticas nacionales es el núcleo desde el que se expande la visión estructuralista hacia un keynesianismo global y una estrategia de desarrollo centrada en un gran impulso ambiental.

Horizons 2030: Equality at the centre of sustainable development

The world is living a change of era. The 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals represent the international community’s response to the economic, distributive and environmental imbalances built up under the prevailing development pattern. This document, presented by the Economic Commission for Latin America and the Caribbean (ECLAC) to its member States at its thirty-sixth session, provides an analytical complement to the 2030 Agenda from a structuralist perspective and from the point of view of the Latin American and Caribbean countries. The proposals made here stem from the need to achieve progressive structural change in order to incorporate more knowledge into production, ensure social inclusion and combat the negative impacts of climate change. The reflections and proposals for advancing towards a new development pattern are geared to achieving equality and environmental sustainability. In these proposals, the creation of global and regional public goods and the corresponding domestic policies form the core for expanding the structuralist tradition towards a global Keynesianism and a development strategy centred around an environmental big push.

Horizontes 2030: a igualdade no centro do desenvolvimento sustentável

O mundo vive uma mudança de época. A comunidade internacional, respondendo aos desequilíbrios econômicos, distributivos e ambientais do estilo de desenvolvimento dominante, aprovou recentemente a Agenda 2030 para o Desenvolvimento Sustentável e seus 17 Objetivos. Este documento, que a Comissão Econômica para a América Latina e o Caribe (CEPAL) apresenta aos Estados membros no trigésimo sexto período de sessões, complementa analiticamente essa Agenda com base na perspectiva estruturalista do desenvolvimento e sob o ponto de vista dos países da América Latina e do Caribe. Suas propostas se concentram na necessidade de impulsionar uma mudança estrutural progressiva que aumente a incorporação de conhecimento na produção, garanta a inclusão social e combata os efeitos negativos da mudança climática. As reflexões e propostas para avançar rumo a um novo estilo de desenvolvimento mantêm seu foco no impulso à igualdade e à sustentabilidade ambiental. A criação de bens públicos globais e de seus correlatos no âmbito regional e de políticas nacionais é o núcleo a partir do qual se expande a visão estruturalista para um keynesianismo global e uma estratégia de desenvolvimento concentrada num grande impulso ambiental.

NodePM: A Remote Monitoring Alert System for Energy Consumption Using Probabilistic Techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Classificação de tábuas de madeira usando processamento de imagens digitais e aprendizado de máquina

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Interface para classificação de dados por máquinas de vetores de suporte

In this project the Pattern Recognition Problem is approached with the Support Vector Machines (SVM) technique, a binary method of classification that provides the best solution separating the data in the better way with a hiperplan and an extension of the input space dimension, as a Machine Learning solution. The system aims to classify two classes of pixels chosen by the user in the interface in the interest selection phase and in the background selection phase, generating all the data to be used in the LibSVM library, a library that implements the SVM, illustrating the library operation in a casual way. The data provided by the interface is organized in three types, RGB (Red, Green and Blue color system), texture (calculated) or RGB + texture. At last the project showed successful results, where the classification of the image pixels was showed as been from one of the two classes, from the interest selection area or from the background selection area. The simplest user view of results classification is the RGB type of data arrange, because it’s the most concrete way of data acquisition