Skewed Evolving Data Streams Classification with Actionable Knowledge Extraction using Data Approximation and Adaptive Classification Framework

Skewed evolving data stream (SEDS) classification is a challenging research problem for online streaming data applications. The fundamental challenges in streaming data classification are class imbalance and concept drift. However, recently, either independently or together, the two topics have received enough attention; the data redundancy while performing stream data mining and classification remains unexplored. Moreover, the existing solutions for the classification of SEDSs have focused on solving concept drift and/or class imbalance problems using the sliding window mechanism, which leads to higher computational complexity and data redundancy problems. To end this, we propose a novel Adaptive Data Stream Classification (ADSC) framework for solving the concept drift, class imbalance, and data redundancy problems with higher computational and classification efficiency. Data approximation, adaptive clustering, classification, and actionable knowledge extraction are the major phases of ADSC. For the purpose of approximating unique items in the data stream with data pre-processing during the data approximation phase, we develop the Flajolet Martin (FM) algorithm. The periodically approximated tuples are grouped into distinct classes using an adaptive clustering algorithm to address the problem of concept drift and class imbalance. In the classification phase, the supervised classifiers are employed to classify the unknown incoming data streams into either of the classes discovered by the adaptive clustering algorithm. We then extract the actionable knowledge using classified skewed evolved data stream information for the end user decision-making process. The ADSC framework is empirically assessed utilizing two streaming datasets regarding classification and computing efficiency factors. The experimental results shows the better efficiency of the proposed ADSC framework as compared with existing classification methods

Heterogeneous ensemble selection for evolving data streams.

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model. In contrast, by combining several types of different learning models, a heterogeneous ensemble system can achieve greater diversity among its members, which helps to improve its performance. Although heterogeneous ensemble systems have achieved many successes in the batch classification setting, it is not trivial to extend them directly to the data stream setting. In this study, we propose a novel HEterogeneous Ensemble Selection (HEES) method, which dynamically selects an appropriate subset of base classifiers to predict data under the stream setting. We are inspired by the observation that a well-chosen subset of good base classifiers may outperform the whole ensemble system. Here, we define a good candidate as one that expresses not only high predictive performance but also high confidence in its prediction. Our selection process is thus divided into two sub-processes: accurate-candidate selection and confident-candidate selection. We define an accurate candidate in the stream context as a base classifier with high accuracy over the current concept, while a confident candidate as one with a confidence score higher than a certain threshold. In the first sub-process, we employ the prequential accuracy to estimate the performance of a base classifier at a specific time, while in the latter sub-process, we propose a new measure to quantify the predictive confidence and provide a method to learn the threshold incrementally. The final ensemble is formed by taking the intersection of the sets of confident classifiers and accurate classifiers. Experiments on a wide range of data streams show that the proposed method achieves competitive performance with lower running time in comparison to the state-of-the-art online ensemble methods

Learning from Data Streams: An Overview and Update

The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that they cannot be met in the contexts of supervised learning. Algorithms are chosen and designed based on criteria which are often not clearly stated, for problem settings not clearly defined, tested in unrealistic settings, and/or in isolation from related approaches in the wider literature. This puts into question the potential for real-world impact of many approaches conceived in such contexts, and risks propagating a misguided research focus. We propose to tackle these issues by reformulating the fundamental definitions and settings of supervised data-stream learning with regard to contemporary considerations of concept drift and temporal dependence; and we take a fresh look at what constitutes a supervised data-stream learning task, and a reconsideration of algorithms that may be applied to tackle such tasks. Through and in reflection of this formulation and overview, helped by an informal survey of industrial players dealing with real-world data streams, we provide recommendations. Our main emphasis is that learning from data streams does not impose a single-pass or online-learning approach, or any particular learning regime; and any constraints on memory and time are not specific to streaming. Meanwhile, there exist established techniques for dealing with temporal dependence and concept drift, in other areas of the literature. For the data streams community, we thus encourage a shift in research focus, from dealing with often-artificial constraints and assumptions on the learning mode, to issues such as robustness, privacy, and interpretability which are increasingly relevant to learning in data streams in academic and industrial settings

Adaptive random forests for evolving data stream classification

Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures on how to evaluate these algorithms. This work presents a taxonomy of algorithms for imbalanced data streams and proposes a standardized, exhaustive, and informative experimental testbed to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to the largest experimental study conducted so far in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental testbed is fully reproducible and easy to extend with new methods. This way we propose the first standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create trustworthy and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams

A survey on online active learning

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in the context of online active learning. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research. Our review aims to provide a comprehensive and up-to-date overview of the field and to highlight directions for future work

Multi-label classification via incremental clustering on an evolving data stream.

With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings

Ensemble Dynamics in Non-stationary Data Stream Classification

Data stream classification is the process of learning supervised models from continuous labelled examples in the form of an infinite stream that, in most cases, can be read only once by the data mining algorithm. One of the most challenging problems in this process is how to learn such models in non-stationary environments, where the data/class distribution evolves over time. This phenomenon is called concept drift. Ensemble learning techniques have been proven effective adapting to concept drifts. Ensemble learning is the process of learning a number of classifiers, and combining them to predict incoming data using a combination rule. These techniques should incrementally process and learn from existing data in a limited memory and time to predict incoming instances and also to cope with different types of concept drifts including incremental, gradual, abrupt or recurring. A sheer number of applications can benefit from data stream classification from non-stationary data, including weather forecasting, stock market analysis, spam filtering systems, credit card fraud detection, traffic monitoring, sensor data analysis in Internet of Things (IoT) networks, to mention a few. Since each application has its own characteristics and conditions, it is difficult to introduce a single approach that would be suitable for all problem domains. This chapter studies ensembles’ dynamic behaviour of existing ensemble methods (e.g. addition, removal and update of classifiers) in non-stationary data stream classification. It proposes a new, compact, yet informative formalisation of state-of-the-art methods. The chapter also presents results of our experiments comparing a diverse selection of best performing algorithms when applied to several benchmark data sets with different types of concept drifts from different problem domains