UM ESTUDO DE MAPEAMENTO SISTEMÁTICO DA MINERAÇÃO DE DADOS PARA CENÁRIOS DE BIG DATA

O volume de dados produzidos tem crescido em larga escala nos últimos anos. Esses dados são de diferentes fontes e diversificados formatos, caracterizando as principais dimensões do Big Data: grande volume, alta velocidade de crescimento e grande variedade de dados. O maior desafio é como gerar informação de qualidade para inferir insights significativos de tais dados variados e grandes. A Mineração de Dados é o processo de identificar, em dados, padrões válidos, novos e potencialmente úteis. No entanto, a infraestrutura de tecnologia da informação tradicional não é capaz de atender as demandas deste novo cenário. O termo atualmente conhecido como Big Data Mining refere-se à extração de informação a partir de grandes bases de dados. Uma questão a ser respondida é como a comunidade científica está abordando o processo de Big Data Mining? Seria válido identificar quais tarefas, métodos e algoritmos vêm sendo aplicados para extrair conhecimento neste contexto. Este artigo tem como objetivo identificar na literatura os trabalhos de pesquisa já realizados no contexto do Big Data Mining. Buscou-se identificar as áreas mais abordadas, os tipos de problemas tratados, as tarefas aplicadas na extração de conhecimento, os métodos aplicados para a realização das tarefas, os algoritmos para a implementação dos métodos, os tipos de dados que vêm sendo minerados, fonte e estrutura dos mesmos. Um estudo de mapeamento sistemático foi conduzido, foram examinados 78 estudos primários. Os resultados obtidos apresentam uma compreensão panorâmica da área investigada, revelando as principais tarefas, métodos e algoritmos aplicados no Big Data Mining

Designing multiple classifier combinations a survey

Classification accuracy can be improved through multiple classifier approach. It has been proven that multiple classifier combinations can successfully obtain better classification accuracy than using a single classifier. There are two main problems in designing a multiple classifier combination which are determining the classifier ensemble and combiner construction. This paper reviews approaches in constructing the classifier ensemble and combiner. For each approach, methods have been reviewed and their advantages and disadvantages have been highlighted. A random strategy and majority voting are the most commonly used to construct the ensemble and combiner, respectively. The results presented in this review are expected to be a road map in designing multiple classifier combinations

Hybrid Automated Machine Learning System for Big Data

A lot of machine learning (ML) models and algorithms exist and in designing classification systems, it is often a challenge looking for and selecting the best performing ML algorithm(s) to use for a dataset in a short period of time. Often, one must learn thor-oughly about the data set structure and content, decide whether to use a supervised, semi-supervised or an unsupervised learning strategy, and then investigate, select or design via trial and error a classification or clustering algorithm that would work most accurately for that specific dataset. This can be quite a time consuming and tedious process. Additionally, a classification algorithm may not perform very well with a dataset as compared to using a clustering algorithm. Meta-learning (learning to learn) and automatic ML (autoML) are data mining-based formalisms for modelling evolving conventional ML functions and toolkit systems. The concept of modelling a decision tree-based combination of both formalisms as a Hybrid-AutoML toolkit extends that of traditional complex autoML systems. In hybrid-autoML, single or multiple predictive models are built by combining a three-layered decision learning architecture for automatic learning mode and model selection, by engaging formal-isms for selecting from a variety of supervised or unsupervised ML algorithms and generic meta information obtained from varying multi-datasets. The work presented in this thesis aims to study, conceptualize, design and develop this hybrid-autoML toolkit. By extending in the simplest form, some existing methodologies for the model training aspect of autoML systems. The theoretical and experimental development focuses on the extension of autoWeka and use of existing meta-learning, algorithm selection and deci-sion tree concepts. It addresses the issue of efficient ML mode (supervised or unsupervised) and model selection for varying multi-datasets, learning methods representations of practical alternative use cases and structuring of layered decision ML un-folding, and algorithms for constructing the unfolding. The im-plementation aims to develop tools for hybrid-autoML based model visualization or evaluation, use case simulations and analysis on single or multi varying datasets. An open source tool called hybrid-autoML has been developed to support these functionali-ties. Hybrid-autoML provides a user-friendly graphical interface that facilitates single or multi varying datasets entry, sup-ports automatic learning mode or strategy selection, automatic model selection on single or multi-varying datasets, supports predictive testing, and allows the automatic visualization and use of a set of analytical tools for model evaluation. It is highly extensible and saves a lot of time

A Classifier Ensemble Framework for Multimedia Big Data Classification

Numerous classification algorithms have been developed for a variety of data types. However, it is nearly impossible for one classifier to perform the best in all kinds of datasets. Therefore, ensemble learning models which aim to take advantages of different classifiers have received a lot of attentions recently. In this paper, a scalable classifier ensemble framework assisted by a set of judgers is proposed to integrate the outputs from multiple classifiers for multimedia big data classification. Specifically, based on the confusion matrices of different classifiers, a set of "judgers" are organized into a hierarchically structured decision model. A testing instance is first input to different classifiers, and then the classification results are passed to the proposed hierarchical structured decision model to derive the final result. The ensemble system can be run on Spark, which is designed for big data processing. Experimental results on multimedia data containing different actions demonstrate that the proposed classifier ensemble framework outperforms several state-of-the-art model fusion approaches