Rule-based preprocessing for data stream mining using complex event processing

Data preprocessing is known to be essential to produce accurate data from which mining methods are able to extract valuable knowledge. When data constantly arrives from one or more sources, preprocessing techniques need to be adapted to efficiently handle these data streams. To help domain experts to define and execute preprocessing tasks for data streams, this paper proposes the use of active rule-based systems and, more specifically, complex event processing (CEP) languages and engines. The main contribution of our approach is the formulation of preprocessing procedures as event detection rules, expressed in an SQL-like language, that provide domain experts a simple way to manipulate temporal data. This idea is materialized into a publicly available solution that integrates a CEP engine with a library for online data mining. To evaluate our approach, we present three practical scenarios in which CEP rules preprocess data streams with the aim of adding temporal information, transforming features and handling missing values. Experiments show how CEP rules provide an effective language to express preprocessing tasks in a modular and high-level manner, without significant time and memory overheads. The resulting data streams do not only help improving the predictive accuracy of classification algorithms, but also allow reducing the complexity of the decision models and the time needed for learning in some cases

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

The ABACOC Algorithm: a Novel Approach for Nonparametric Classification of Data Streams

Stream mining poses unique challenges to machine learning: predictive models are required to be scalable, incrementally trainable, must remain bounded in size (even when the data stream is arbitrarily long), and be nonparametric in order to achieve high accuracy even in complex and dynamic environments. Moreover, the learning system must be parameterless ---traditional tuning methods are problematic in streaming settings--- and avoid requiring prior knowledge of the number of distinct class labels occurring in the stream. In this paper, we introduce a new algorithmic approach for nonparametric learning in data streams. Our approach addresses all above mentioned challenges by learning a model that covers the input space using simple local classifiers. The distribution of these classifiers dynamically adapts to the local (unknown) complexity of the classification problem, thus achieving a good balance between model complexity and predictive accuracy. We design four variants of our approach of increasing adaptivity. By means of an extensive empirical evaluation against standard nonparametric baselines, we show state-of-the-art results in terms of accuracy versus model size. For the variant that imposes a strict bound on the model size, we show better performance against all other methods measured at the same model size value. Our empirical analysis is complemented by a theoretical performance guarantee which does not rely on any stochastic assumption on the source generating the stream

Considering Currency in Decision Trees in the Context of Big Data

In the current age of big data, decision trees are one of the most commonly applied data mining methods. However, for reliable results they require up-to-date input data, which is not always given in reality. We present a two-phase approach based on probability theory for considering currency of stored data in decision trees. Our approach is efficient and thus suitable for big data applications. Moreover, it is independent of the particular decision tree classifier. Finally, it is context-specific since the decision tree structure and supplemental data are taken into account. We demonstrate the benefits of the novel approach by applying it to three datasets. The results show a substantial increase in the classification success rate as opposed to not considering currency. Thus, applying our approach prevents wrong classification and consequently wrong decisions

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed