Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding, and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.

Learning under Concept Drift: A Review

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.

A Review on Outlier/Anomaly Detection in Time Series Data

Incremental on-line learning: A review and comparison of state of the art algorithms

Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.

Traffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application’s name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine learning (ML) gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. ML is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using ML techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for ML-based traffic classification.

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Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

Data Mining in non-stationary data streams is gaining more attentionrecently, especially in the context of Internet of Things and Big Data. It is a highly challenging task, since the fundamentally different typesof possibly occurring drift undermine classical assumptions such asi.i.d. data or stationary distributions. Available algorithms are either struggling with certain forms of drift or require a priori knowledge in terms of a task specific setting. We propose the Self Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm since kNN constitutes a proven classifier within the streaming setting. SAM-kNN can deal with heterogeneous concept drift, i.e different drift types and rates, using biologically inspiredmemory models and their coordination. It can be easilyapplied in practice since an optimization of the meta parameters is not necessary. The basic idea is to construct dedicated models for thecurrent and former concepts and apply them according tothe demands of the given situation. An extensive evaluation on various benchmarks, consisting of artificial streamswith known drift characteristics as well as real world datasets is conducted. Thereby, we explicitly add new benchmarks enabling a precise performance evaluation on multiple types of drift. The highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capabilityto handle heterogeneous concept drift.

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KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift

We present an architecture for incremental online learning in high-dimensional feature spaces and apply it on a mobile robot. The model is based on learning vector quantization, approaching the stability-plasticity problem of incremental learning by adaptive insertions of representative vectors. We employ a cost-function-based learning vector quantization approach and introduce a new insertion strategy optimizing a cost-function based on a subset of samples. We demonstrate this model within a real-time application for a mobile robot scenario, where we perform interactive real-time learning of visual categories.

/pdf/interactive-online-learning-for-obstacle-classification-on-a-1n8waktuln.pdf

Interactive online learning for obstacle classification on a mobile robot

Data mining in non-stationary data streams is particularly relevant in the context of Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self-Adjusting Memory (SAM) model for the k-nearest-neighbor (kNN) algorithm. SAM-kNN can deal with heterogeneous concept drift, i.e., different drift types and rates, using biologically inspired memory models and their coordination. Its basic idea is to have dedicated models for current and former concepts used according to the demands of the given situation. It can be easily applied in practice without meta parameter optimization. We conduct an extensive evaluation on various benchmarks, consisting of artificial streams with known drift characteristics and real-world datasets. We explicitly add new benchmarks enabling a precise performance analysis on multiple types of drift. Highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capability to handle heterogeneous concept drift. Knowledge about drift characteristics in streaming data is not only crucial for a precise algorithm evaluation, but it also facilitates the choice of an appropriate algorithm on real-world applications. Therefore, we additionally propose two tests, able to determine the type and strength of drift. We extract the drift characteristics of all utilized datasets and use it for our analysis of the SAM in relation to other methods.

Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)

Recently, incremental and on-line learning gained more attention 
especially in the context of big data and learning from data streams,
conflicting with the traditional assumption of complete data availability. Even though 
a variety of different methods are available, it often remains unclear which of them is suitable
for a specific task and how they perform in comparison to each other. 
We analyze the key properties of seven incremental methods representing different algorithm classes.
Our extensive evaluation on data sets with different characteristics gives an overview of the performance with respect to accuracy as well as model complexity,
facilitating the choice of the best method for a given application.

Viktor Losing

Papers

Incremental on-line learning: A review and comparison of state of the art algorithms

KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift

Interactive online learning for obstacle classification on a mobile robot

Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM)

Choosing the Best Algorithm for an Incremental On-line Learning Task