Neuroevolutionary learning in nonstationary environments

This work presents a new neuro-evolutionary model, called NEVE (Neuroevolutionary Ensemble), based on an ensemble of Multi-Layer Perceptron (MLP) neural networks for learning in nonstationary environments. NEVE makes use of quantum-inspired evolutionary models to automatically configure the ensemble members and combine their output. The quantum-inspired evolutionary models identify the most appropriate topology for each MLP network, select the most relevant input variables, determine the neural network weights and calculate the voting weight of each ensemble member. Four different approaches of NEVE are developed, varying the mechanism for detecting and treating concepts drifts, including proactive drift detection approaches. The proposed models were evaluated in real and artificial datasets, comparing the results obtained with other consolidated models in the literature. The results show that the accuracy of NEVE is higher in most cases and the best configurations are obtained using some mechanism for drift detection. These results reinforce that the neuroevolutionary ensemble approach is a robust choice for situations in which the datasets are subject to sudden changes in behaviour

Incremental learning of concept drift from imbalanced data

Learning data sampled from a nonstationary distribution has been shown to be a very challenging problem in machine learning, because the joint probability distribution between the data and classes evolve over time. Thus learners must adapt their knowledge base, including their structure or parameters, to remain as strong predictors. This phenomenon of learning from an evolving data source is akin to learning how to play a game while the rules of the game are changed, and it is traditionally referred to as learning concept drift. Climate data, financial data, epidemiological data, spam detection are examples of applications that give rise to concept drift problems. An additional challenge arises when the classes to be learned are not represented (approximately) equally in the training data, as most machine learning algorithms work well only when the class distributions are balanced. However, rare categories are commonly faced in real-world applications, which leads to skewed or imbalanced datasets. Fraud detection, rare disease diagnosis, anomaly detection are examples of applications that feature imbalanced datasets, where data from category are severely underrepresented. Concept drift and class imbalance are traditionally addressed separately in machine learning, yet data streams can experience both phenomena. This work introduces Learn++.NIE (nonstationary & imbalanced environments) and Learn++.CDS (concept drift with SMOTE) as two new members of the Learn++ family of incremental learning algorithms that explicitly and simultaneously address the aforementioned phenomena. The former addresses concept drift and class imbalance through modified bagging-based sampling and replacing a class independent error weighting mechanism - which normally favors majority class - with a set of measures that emphasize good predictive accuracy on all classes. The latter integrates Learn++.NSE, an algorithm for concept drift, with the synthetic sampling method known as SMOTE, to cope with class imbalance. This research also includes a thorough evaluation of Learn++.CDS and Learn++.NIE on several real and synthetic datasets and on several figures of merit, showing that both algorithms are able to learn in some of the most difficult learning environments

COMPOSE: Compacted object sample extraction a framework for semi-supervised learning in nonstationary environments

An increasing number of real-world applications are associated with streaming data drawn from drifting and nonstationary distributions. These applications demand new algorithms that can learn and adapt to such changes, also known as concept drift. Proper characterization of such data with existing approaches typically requires substantial amount of labeled instances, which may be difficult, expensive, or even impractical to obtain. In this thesis, compacted object sample extraction (COMPOSE) is introduced - a computational geometry-based framework to learn from nonstationary streaming data - where labels are unavailable (or presented very sporadically) after initialization. The feasibility and performance of the algorithm are evaluated on several synthetic and real-world data sets, which present various different scenarios of initially labeled streaming environments. On carefully designed synthetic data sets, we also compare the performance of COMPOSE against the optimal Bayes classifier, as well as the arbitrary subpopulation tracker algorithm, which addresses a similar environment referred to as extreme verification latency. Furthermore, using the real-world National Oceanic and Atmospheric Administration weather data set, we demonstrate that COMPOSE is competitive even with a well-established and fully supervised nonstationary learning algorithm that receives labeled data in every batch

Incremental Learning on Non-stationary Data Stream using Ensemble Approach

Incremental Learning on non stationary distribution has been shown to be a very challenging problem in machine learning and data mining, because the joint probability distribution between the data and classes changes over time. Many real time problems suffer concept drift as they changes with time. For example, an advertisement recommendation system, in which customer’s behavior may change depending on the season of the year, on the inflation and on new products made available. An extra challenge arises when the classes to be learned are not represented equally in the training data i.e. classes are imbalanced, as most machine learning algorithms work well only when the training data  is balanced. The objective of this paper is to develop an ensemble based classification algorithm for non-stationary data stream (ENSDS) with focus on two-class problems. In addition, we are presenting here an exhaustive comparison of purposed algorithms with state-of-the-art classification approaches using different evaluation measures like recall, f-measure and g-mea

Concept Drift Detection in Data Stream Mining: The Review of Contemporary Literature

Mining process such as classification, clustering of progressive or dynamic data is a critical objective of the information retrieval and knowledge discovery; in particular, it is more sensitive in data stream mining models due to the possibility of significant change in the type and dimensionality of the data over a period. The influence of these changes over the mining process termed as concept drift. The concept drift that depict often in streaming data causes unbalanced performance of the mining models adapted. Hence, it is obvious to boost the mining models to predict and analyse the concept drift to achieve the performance at par best. The contemporary literature evinced significant contributions to handle the concept drift, which fall in to supervised, unsupervised learning, and statistical assessment approaches. This manuscript contributes the detailed review of the contemporary concept-drift detection models depicted in recent literature. The contribution of the manuscript includes the nomenclature of the concept drift models and their impact of imbalanced data tuples