Dual-Tree Complex Wavelet Packet Transform and Feature Selection Techniques for Infant Cry Classification

A Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction has been used in infant cry signal classification to extract the feature. Total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band after five level decomposition by DT-CWPT. Feature selection techniques used to deal with massive information obtained from DT-CWPT extraction. The feature selection techniques reduced the number of features by select and form feature subset for classification phase. ELM classifier with 10-fold cross-validation scheme was used to classify the infant cry signal. Three experiments were conducted with different feature sets for three binary classification problems (Asphyxia versus Normal, Deaf versus Normal, and Hunger versus Pain). The results reported that features selection techniques reduced the number of features and achieved high accuracy

Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms

Wearable inertial sensors are currently receiving pronounced interest due to applications in unconstrained daily life settings, ambulatory monitoring and pervasive computing systems. This research focuses on human activity recognition problem, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are automatically classified human activities. A general-purpose framework has been presented for designing and evaluating activity recognition system with six different activities using machine learning algorithms such as support vector machine (SVM) and artificial neural networks (ANN). Several feature selection methods were explored to make the recognition process faster by experimenting on the features extracted from the accelerometer and gyroscope time series data collected from a number of volunteers. In addition, a detailed discussion is presented to explore how different design parameters, for example, the number of features and data fusion from multiple sensor locations - impact on overall recognition performance

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques

Concept Drift Adaptation in Text Stream Mining Settings: A Comprehensive Review

Due to the advent and increase in the popularity of the Internet, people have been producing and disseminating textual data in several ways, such as reviews, social media posts, and news articles. As a result, numerous researchers have been working on discovering patterns in textual data, especially because social media posts function as social sensors, indicating peoples' opinions, interests, etc. However, most tasks regarding natural language processing are addressed using traditional machine learning methods and static datasets. This setting can lead to several problems, such as an outdated dataset, which may not correspond to reality, and an outdated model, which has its performance degrading over time. Concept drift is another aspect that emphasizes these issues, which corresponds to data distribution and pattern changes. In a text stream scenario, it is even more challenging due to its characteristics, such as the high speed and data arriving sequentially. In addition, models for this type of scenario must adhere to the constraints mentioned above while learning from the stream by storing texts for a limited time and consuming low memory. In this study, we performed a systematic literature review regarding concept drift adaptation in text stream scenarios. Considering well-defined criteria, we selected 40 papers to unravel aspects such as text drift categories, types of text drift detection, model update mechanism, the addressed stream mining tasks, types of text representations, and text representation update mechanism. In addition, we discussed drift visualization and simulation and listed real-world datasets used in the selected papers. Therefore, this paper comprehensively reviews the concept drift adaptation in text stream mining scenarios.Comment: 49 page

Modelagem simbólica de padrões morfológicos para classificação de séries temporais

Orientador : Prof. Dr. Fabiano SilvaTese (Doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 14/09/2015Inclui referências : f. 149-167Resumo: O contínuo armazenamento de dados ao longo do tempo, tais como séries temporais, tem motivado o desenvolvimento de novas abordagens baseadas em métodos de mineração de dados. Nesse cenário, uma nova área de pesquisa emergiu durante as últimas duas décadas, a mineração de dados em séries temporais. Mais especificamente, as abordagens baseadas em técnicas de aprendizado de máquina têm apresentado maior interesse entre os pesquisadores. Dentre as tarefas de mineração de dados, a classificação de séries temporais tem sido amplamente explorada, de modo que estudos recentes, utilizando algoritmos de aprendizado não simbólicos, têm reportado resultados significativos, em termos da acurácia de classificação. No entanto, em aplicações que envolvem processos de auxílio à tomada de decisão, tais como diagnóstico médico, controle de produção industrial, sistemas de monitoração de segurança em aeronaves ou usinas de energia elétrica, é necessário possibilitar o entendimento do raciocínio utilizado no processo de classificação. A primitiva shapelet foi proposta na literatura como um descritor de características morfológicas locais para possibilitar melhor compreensão dos conceitos, devido a sua maior proximidade com a percepção humana na identificação de padrões em séries temporais. Contudo, a maioria dos trabalhos relacionados ao estudo dessa primitiva tem se dedicado ao desenvolvimento de abordagens mais eficientes em termos de tempo e de acurácia, desconsiderando a necessidade da inteligibilidade dos classificadores. Nesse contexto, neste trabalho foi proposto um método que utiliza a transformada shapelet para a construção de modelos simbólicos de classificação por meio de uma abordagem híbrida que combina a representação de árvore de decisão com o algoritmo vizinho mais próximo. Também, foram desenvolvidas estratégias para melhorar a qualidade de representação da transformada shapelet na utilização de classificadores simbólicos, como árvores de decisão. Para avaliar o desempenho dessas propostas, foi conduzida uma avaliação experimental que envolveu a comparação com os algoritmos considerados estado da arte usando conjuntos de dados amplamente estudados na literatura de classificação de séries temporais. Com base nos resultados e análises realizadas nesta tese, foi possível verificar que a melhoria do processo de identificação de shapelets possibilita a construção de classificadores inteligíveis e competitivos; e que métodos híbridos podem contribuir para prover uma representação simbólica dos modelos, com desempenho equivalente ou até mesmo superior aos métodos não simbólicos. Palavras-chave: mineração de dados. aprendizado de máquina. séries temporais. classificação. modelos simbólicos.Abstract: The large amount of stored data over time, such as time series, has motivated the development of new approaches based on data mining methods. In this context, a new research area has emerged over the last two decades, the time series data mining. In particular, the approaches based on machine learning techniques have shown large interest among researchers. Among the data mining tasks, the time series classification has been widely exploited. Recent studies using non-symbolic learning algorithms have reported significant results in terms of classification accuracy. However, in applications related to decision making process, such as medical diagnosis, industrial production control, security monitoring systems in aircraft and in power plants, it is necessary allow the understanding of the reasoning used in the classification process. To take this into account, the shapelet primitive has been proposed in the literature as a descriptor of local morphological characteristics, which is closer to human perception for patterns identification in time series. On the other hand, most of the existing work related to shapelets has been dedicated to the development of more effective approaches in terms of time and accuracy, disregarding the need for interpretability of the classifiers. In this work, we propose to build symbolic models for time series classification using the shapelet transformation. This method is based on a hybrid approach that merges the decision tree representation and the nearest neighbor algorithm. Also, we developed strategies to improve the representation quality of the shapelet transformation using feature selection algorithms. We performed an experimental evaluation to analyze the performance of our proposals in comparison to the algorithms considered state of the art using datasets widely studied in the literature of time series classification. Based on the results and analysis carried out in this thesis, we found that the improvement of shapelet representation allows the construction of interpretable and competitive classifiers. Moreover, we found that the hybrid methods can help to provide symbolic models with equivalent or even superior performance to non-symbolic methods. Keywords: data mining. machine learning. time series. classification. symbolic models