Progress in Artificial Intelligence 

About: Progress in Artificial Intelligence is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Computational intelligence. It has an ISSN identifier of 2192-6352. Over the lifetime, 294 publications have been published receiving 6195 citations.

Learning from imbalanced data: open challenges and future directions

Abstract: Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.

Convolutional neural network: a review of models, methodologies and applications to object detection

Abstract: Deep learning has developed as an effective machine learning method that takes in numerous layers of features or representation of the data and provides state-of-the-art results. The application of deep learning has shown impressive performance in various application areas, particularly in image classification, segmentation and object detection. Recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we provide a detailed review of various deep architectures and model highlighting characteristics of particular model. Firstly, we described the functioning of CNN architectures and its components followed by detailed description of various CNN models starting with classical LeNet model to AlexNet, ZFNet, GoogleNet, VGGNet, ResNet, ResNeXt, SENet, DenseNet, Xception, PNAS/ENAS. We mainly focus on the application of deep learning architectures to three major applications, namely (i) wild animal detection, (ii) small arm detection and (iii) human being detection. A detailed review summary including the systems, database, application and accuracy claimed is also provided for each model to serve as guidelines for future work in the above application areas.

Event labeling combining ensemble detectors and background knowledge

Abstract: Event labeling is the process of marking events in unlabeled data. Traditionally, this is done by involving one or more human experts through an expensive and time- consuming task. In this article we propose an event label- ing system relying on an ensemble of detectors and back- ground knowledge. The target data are the usage log of a real bike sharing system. We first label events in the data and then evaluate the performance of the ensemble and indi- vidual detectors on the labeled data set using ROC analysis and static evaluation metrics in the absence and presence of background knowledge. Our results show that when there is no access to human experts, the proposed approach can be an effective alternative for labeling events. In addition to the main proposal, we conduct a comparative study regarding the various predictive models performance, semi-supervised and unsupervised approaches, train data scale, time series filtering methods, online and offline predictive models, and distance functions in measuring time series similarity.

Learning from streaming data with concept drift and imbalance: an overview

Abstract: The primary focus of machine learning has traditionally been on learning from data assumed to be sufficient and representative of the underlying fixed, yet unknown, distribution. Such restrictions on the problem domain paved the way for development of elegant algorithms with theoretically provable performance guarantees. As is often the case, however, real-world problems rarely fit neatly into such restricted models. For instance class distributions are often skewed, resulting in the “class imbalance” problem. Data drawn from non-stationary distributions is also common in real-world applications, resulting in the “concept drift” or “non-stationary learning” problem which is often associated with streaming data scenarios. Recently, these problems have independently experienced increased research attention, however, the combined problem of addressing all of the above mentioned issues has enjoyed relatively little research. If the ultimate goal of intelligent machine learning algorithms is to be able to address a wide spectrum of real-world scenarios, then the need for a general framework for learning from, and adapting to, a non-stationary environment that may introduce imbalanced data can be hardly overstated. In this paper, we first present an overview of each of these challenging areas, followed by a comprehensive review of recent research for developing such a general framework.

Binary relevance efficacy for multilabel classification

Abstract: The goal of multilabel (ML) classification is to induce models able to tag objects with the labels that better describe them The main baseline for ML classification is binary relevance (BR), which is commonly criticized in the literature because of its label independence assumption Despite this fact, this paper discusses some interesting properties of BR, mainly that it produces optimal models for several ML loss functions Additionally, we present an analytical study of ML benchmarks datasets and point out some shortcomings As a result, this paper proposes the use of synthetic datasets to better analyze the behavior of ML methods in domains with different characteristics To support this claim, we perform some experiments using synthetic data proving the competitive performance of BR with respect to a more complex method in difficult problems with many labels, a conclusion which was not stated by previous studies