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Early classification of time series assigns each time series to one of a set of pre-defined classes using as few measurements as possible while preserving a high accuracy. This implies solving online the trade-off between the earliness and the prediction accuracy. This has been formalized in previous work where a cost-based framework taking into account both the cost of misclassification and the cost of delaying the decision has been proposed. The best resulting method, called Economy- $\gamma$ , is unfortunately so far limited to binary classification problems. This paper presents a set of six new methods that extend the Economy- $\gamma$ method in order to solve multiclass classification problems. Extensive experiments on 33 datasets allowed us to compare the performance of the six proposed approaches to the state-of-the-art one. The results show that: (i) all proposed methods perform significantly better than the state of the art one; (ii) the best way to extend Economy- $\gamma$ to multiclass problems is to use a confidence score, either the Gini index or the maximum probability.

The field of Weakly Supervised Learning (WSL) has recently seen a surge of popularity, with numerous papers addressing different types of "supervision deficiencies". In WSL use cases, a variety of situations exists where the collected "information" is imperfect. The paradigm of WSL attempts to list and cover these problems with associated solutions. In this paper, we review the research progress on WSL with the aim to make it as a brief introduction to this field. We present the three axis of WSL cube and an overview of most of all the elements of their facets. We propose three measurable quantities that acts as coordinates in the previously defined cube namely: Quality, Adaptability and Quantity of information. Thus we suggest that Biquality Learning framework can be defined as a plan of the WSL cube and propose to re-discover previously unrelated patches in WSL literature as a unified Biquality Learning literature.

The incoming smart grid represents a significant break for the European utilities in terms of data volume to be processed. In France, one year of individual consumptions represents more than 600 billion data points. Since real data is not yet available, our objective consists in simulating realistic individual consumptions. A new generative model of time series is proposed which combines the MODL coclustering approach with Markov chains. This approach is evaluated on a real dataset provided by the Irish CER. Our experiments demonstrate the ability of the generative model to efficiently reproduce the dynamic of the original time series.

In France, the currently emerging “smart grid” and more particularly the 35 millions of “smart meters” will produce a large amount of daily updated metering data. The main french provider of electricity (EDF) is interested by compact and generic representations of time series which allow to accelerate the processing of data. This article proposes a new data-driven symbolic representation of time series named SAXO, where each symbol represents a typical distribution of data points. Furthermore, the time dimension is optimally discretized into intervals by using a parameter free Bayesian coclustering approach (MODL). SAXO is favorably compared with the SAX representation by evaluating a classifier trained from recoded datasets. Our experiments highlight a significant gap in performance between both approaches.

In recent years, the amount of data to process has increased in many application areas such as network monitoring, web click and sensor data analysis. Data stream mining answers to the challenge of massive data processing, this paradigm allows for treating pieces of data on the fly and overcomes exhaustive data storage. The detection of changes in a data stream distribution is an important issue which application area is wide. In this article, change detection problem is turned into a supervised learning task. We chose to exploit the supervised discretization method “MODL” given its interesting properties. Our approach is favorably compared with an alternative method on artificial data streams, and is applied on real data streams.

The labeling of training examples could be a costly task in numerous cases of supervised learning. Active learning strategies address this problem and select unlabeled examples which are considered as the most useful for the training of a predictive model. The choice of examples to be labeled can be considered as a dilemma between the exploration and the exploitation of the input data space. In this article, a new active learning strategy that manages this compromise is proposed. This strategy is based on a Bayesian formalism that minimizes assumptions on data. An experimental validation is conducted on a unidimensional dataset, the objective is to assess the position of a step function from noisy examples. Our approach is favorably compared to an ad hoc strategy : the probabilistic dichotomy.