One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

A Survey Of Methods For Explaining Black Box Models

In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness sometimes at the cost of scarifying accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation. The aim of this paper is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.Comment: This work is currently under review on an international journa

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and Cl benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research. (C) 2011 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved

A survey of methods for explaining black box models

In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective

New perspectives and methods for stream learning in the presence of concept drift.

153 p.Applications that generate data in the form of fast streams from non-stationary environments, that is,those where the underlying phenomena change over time, are becoming increasingly prevalent. In thiskind of environments the probability density function of the data-generating process may change overtime, producing a drift. This causes that predictive models trained over these stream data become obsoleteand do not adapt suitably to the new distribution. Specially in online learning scenarios, there is apressing need for new algorithms that adapt to this change as fast as possible, while maintaining goodperformance scores. Examples of these applications include making inferences or predictions based onfinancial data, energy demand and climate data analysis, web usage or sensor network monitoring, andmalware/spam detection, among many others.Online learning and concept drift are two of the most hot topics in the recent literature due to theirrelevance for the so-called Big Data paradigm, where nowadays we can find an increasing number ofapplications based on training data continuously available, named as data streams. Thus, learning in nonstationaryenvironments requires adaptive or evolving approaches that can monitor and track theunderlying changes, and adapt a model to accommodate those changes accordingly. In this effort, Iprovide in this thesis a comprehensive state-of-the-art approaches as well as I identify the most relevantopen challenges in the literature, while focusing on addressing three of them by providing innovativeperspectives and methods.This thesis provides with a complete overview of several related fields, and tackles several openchallenges that have been identified in the very recent state of the art. Concretely, it presents aninnovative way to generate artificial diversity in ensembles, a set of necessary adaptations andimprovements for spiking neural networks in order to be used in online learning scenarios, and finally, adrift detector based on this former algorithm. All of these approaches together constitute an innovativework aimed at presenting new perspectives and methods for the field