Modeling the Example Life-Cycle in an Online Classification Learner

Abstract. An online classification system maintained by a learner can be subject to latency and filtering of training examples which can impact on its classification accuracy especially under concept drift. A life-cycle model is developed to provide a framework for studying this problem. Meta data emerges from this model which it is proposed can enhance online learning systems. In particular, the definition of the time-stamp of an example, as currently used in the literature, is shown to be problematic and an alternative is proposed

Combining Stream Mining and Neural Networks for Short Term Delay Prediction

The systems monitoring the location of public transport vehicles rely on wireless transmission. The location readings from GPS-based devices are received with some latency caused by periodical data transmission and temporal problems preventing data transmission. This negatively affects identification of delayed vehicles. The primary objective of the work is to propose short term hybrid delay prediction method. The method relies on adaptive selection of Hoeffding trees, being stream classification technique and multilayer perceptrons. In this way, the hybrid method proposed in this study provides anytime predictions and eliminates the need to collect extensive training data before any predictions can be made. Moreover, the use of neural networks increases the accuracy of the predictions compared with the use of Hoeffding trees only

A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams

Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare. To address the learning problems associated with such data, one can ignore the unlabelled data and focus only on the labelled data (supervised learning); use the labelled data and attempt to leverage the unlabelled data (semi-supervised learning); or assume some labels will be available on request (active learning). The first approach is the simplest, yet the amount of labelled data available will limit the predictive performance. The second relies on finding and exploiting the underlying characteristics of the data distribution. The third depends on an external agent to provide the required labels in a timely fashion. This survey pays special attention to methods that leverage unlabelled data in a semi-supervised setting. We also discuss the delayed labelling issue, which impacts both fully supervised and semi-supervised methods. We propose a unified problem setting, discuss the learning guarantees and existing methods, explain the differences between related problem settings. Finally, we review the current benchmarking practices and propose adaptations to enhance them

Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels

One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over time. Concept drift detection aims to detect such drifts and adapt the model so as to mitigate any deterioration in the model's predictive performance. Unfortunately, most existing concept drift detection methods rely on a strong and over-optimistic condition that the true labels are available immediately for all already classified instances. In this paper, a novel Hierarchical Hypothesis Testing framework with Request-and-Reverify strategy is developed to detect concept drifts by requesting labels only when necessary. Two methods, namely Hierarchical Hypothesis Testing with Classification Uncertainty (HHT-CU) and Hierarchical Hypothesis Testing with Attribute-wise "Goodness-of-fit" (HHT-AG), are proposed respectively under the novel framework. In experiments with benchmark datasets, our methods demonstrate overwhelming advantages over state-of-the-art unsupervised drift detectors. More importantly, our methods even outperform DDM (the widely used supervised drift detector) when we use significantly fewer labels.Comment: Published as a conference paper at IJCAI 201

AMANDA : density-based adaptive model for nonstationary data under extreme verification latency scenarios

Gradual concept-drift refers to a smooth and gradual change in the relations between input and output data in the underlying distribution over time. The problem generates a model obsolescence and consequently a quality decrease in predictions. Besides, there is a challenging task during the stream: The extreme verification latency (EVL) to verify the labels. For batch scenarios, state-of-the-art methods propose an adaptation of a supervised model by using an unconstrained least squares importance fitting (uLSIF) algorithm or a semi-supervised approach along with a core support extraction (CSE) method. However, these methods do not properly tackle the mentioned problems due to their high computational time for large data volumes, lack in representing the right samples of the drift or even for having several parameters for tuning. Therefore, we propose a density-based adaptive model for nonstationary data (AMANDA), which uses a semi-supervised classifier along with a CSE method. AMANDA has two variations: AMANDA with a fixed cutting percentage (AMANDA-FCP); and AMANDA with a dynamic cutting percentage (AMANDADCP). Our results indicate that the two variations of AMANDA outperform the state-of-the-art methods for almost all synthetic datasets and real ones with an improvement up to 27.98% regarding the average error. We have found that the use of AMANDA-FCP improved the results for a gradual concept-drift even with a small size of initial labeled data. Moreover, our results indicate that SSL classifiers are improved when they work along with our static or dynamic CSE methods. Therefore, we emphasize the importance of research directions based on this approach.Concept-drift gradual refere-se à mudança suave e gradual na distribuição dos dados conforme o tempo passa. Este problema causa obsolescência no modelo de aprendizado e queda na qualidade das previsões. Além disso, existe um complicador durante o processamento dos dados: a latência de verificação extrema (LVE) para se verificar os rótulos. Métodos do estado da arte propõem uma adaptação do modelo supervisionado usando uma abordagem de estimação de importância baseado em mínimos quadrados ou usando uma abordagem semi-supervisionada em conjunto com a extração de instâncias centrais, na sigla em inglês (CSE). Entretanto, estes métodos não tratam adequadamente os problemas mencionados devido ao fato de requererem alto tempo computacional para processar grandes volumes de dados, falta de correta seleção das instâncias que representam a mudança da distribuição, ou ainda por demandarem o ajuste de grande quantidade de parâmetros. Portanto, propomos um modelo adaptativo baseado em densidades para dados não-estacionários (AMANDA), que tem como base um classificador semi-supervisionado e um método CSE baseado em densidade. AMANDA tem duas variações: percentual de corte fixo (AMANDAFCP); e percentual de corte dinâmico (AMANDA-DCP). Nossos resultados indicam que as duas variações da proposta superam o estado da arte em quase todas as bases de dados sintéticas e reais em até 27,98% em relação ao erro médio. Concluímos que a aplicação do método AMANDA-FCP faz com que a classificação melhore mesmo quando há uma pequena porção inicial de dados rotulados. Mais ainda, os classificadores semi-supervisionados são melhorados quando trabalham em conjunto com nossos métodos de CSE, estático ou dinâmico