Classification of time series is a growing problem in different disciplines due to the progressive digitalization of the world. Currently, the state of the art in time series classification is dominated by Collective of Transformation-Based Ensembles. This algorithm is composed of several classifiers of diverse nature that are combined according to their results in an internal cross validation procedure. Its high complexity prevents it from being applied to large datasets. One Nearest Neighbours with Dynamic Time Warping remains the base classifier in any time series classification problem, for its simplicity and good results. Despite their good performance, they share a weakness, which is that they are not interpretable. In the field of time series classification, there is a tradeoff between accuracy and interpretability. In this work, we propose a set of characteristics capable of extracting information of the structure of the time series in order to face time series classification problems. The use of these characteristics allows the use of traditional classification algorithms in time series problems. The experimental results demonstrate a statistically significant improvement in the accuracy of the results obtained by our proposal with respect to the original time series. Apart from the improvement in accuracy, our proposal is able to offer interpretable results based on the set of characteristics proposed.

Neural networks trained to minimize the logistic (a.k.a. cross-entropy) loss with gradient-based methods are observed to perform well in many supervised classification tasks. Towards understanding this phenomenon, we analyze the training and generalization behavior of infinitely wide two-layer neural networks with homogeneous activations. We show that the limits of the gradient flow on exponentially tailed losses can be fully characterized as a max-margin classifier in a certain non-Hilbertian space of functions. In presence of hidden low-dimensional structures, the resulting margin is independent of the ambiant dimension, which leads to strong generalization bounds. In contrast, training only the output layer implicitly solves a kernel support vector machine, which a priori does not enjoy such an adaptivity. Our analysis of training is non-quantitative in terms of running time but we prove computational guarantees in simplified settings by showing equivalences with online mirror descent. Finally, numerical experiments suggest that our analysis describes well the practical behavior of two-layer neural networks with ReLU activation and confirm the statistical benefits of this implicit bias.

The continued improvements in the predictive accuracy of machine learning models have allowed for their widespread practical application. Yet, many decisions made with seemingly accurate models still require verification by domain experts. In addition, end-users of a model also want to understand the reasons behind specific decisions. Thus, the need for interpretability is increasingly paramount. In this paper we present an interactive visual analytics tool, ViCE, that generates counterfactual explanations to contextualize and evaluate model decisions. Each sample is assessed to identify the minimal set of changes needed to flip the model's output. These explanations aim to provide end-users with personalized actionable insights with which to understand, and possibly contest or improve, automated decisions. The results are effectively displayed in a visual interface where counterfactual explanations are highlighted and interactive methods are provided for users to explore the data and model. The functionality of the tool is demonstrated by its application to a home equity line of credit dataset.

Intrusion detection system (IDS) plays an essential role in computer networks protecting computing resources and data from outside attacks. Recent IDS faces challenges improving flexibility and efficiency of the IDS for unexpected and unpredictable attacks. Deep neural network (DNN) is considered popularly for complex systems to abstract features and learn as a machine learning technique. In this paper, we propose a deep learning approach for developing the efficient and flexible IDS using one-dimensional Convolutional Neural Network (1D-CNN). Two-dimensional CNN methods have shown remarkable performance in detecting objects of images in computer vision area. Meanwhile, the 1D-CNN can be used for supervised learning on time-series data. We establish a machine learning model based on the 1D-CNN by serializing Transmission Control Protocol/Internet Protocol (TCP/IP) packets in a predetermined time range as an invasion Internet traffic model for the IDS, where normal and abnormal network traffics are categorized and labeled for supervised learning in the 1D-CNN. We evaluated our model on UNSW\_NB15 IDS dataset to show the effectiveness of our method. For comparison study in performance, machine learning-based Random Forest (RF) and Support Vector Machine (SVM) models in addition to the 1D-CNN with various network parameters and architecture are exploited. In each experiment, the models are run up to 200 epochs with a learning rate in 0.0001 on imbalanced and balanced data. 1D-CNN and its variant architectures have outperformed compared to the classical machine learning classifiers. This is mainly due to the reason that CNN has the capability to extract high-level feature representations that represent the abstract form of low-level feature sets of network traffic connections.

Many machine learning models are capable of predicting a probability or probability-like scores for class membership. Probabilities provide a required level of granularity for evaluating and comparing models, especially on imbalanced classification problems where tools like ROC Curves are used to interpret predictions and the ROC AUC metric is used to compare model performance, both of which use probabilities. Unfortunately, the probabilities or probability-like scores predicted by many models are not calibrated. This means that they may be over-confident in some cases and under-confident in other cases. Worse still, the severely skewed class distribution present in imbalanced classification tasks may result in even more bias in the predicted probabilities as they over-favor predicting the majority class. As such, it is often a good idea to calibrate the predicted probabilities for nonlinear machine learning models prior to evaluating their performance.

If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here? In Machine Learning, there's no single solution that can fit all and multiple solutions to a problem can exist. With lots of varieties of algorithms, choosing the right algorithm for your problem can become a daunting task. Don't worry! in this article, we will be simplifying your approach in Machine Learning with a cheat sheet that you can use to select the right algorithm suited for your problem. There are several factors that can affect the decision of choosing the right algorithm.

All email providers today offer a special mail category known as'Spam', but how does the email program figure out the criteria for this type of categorization? The answer lies in Machine Learning. It could also be based on certain specific/typical patterns of email releases or roll outs. So when you receive a spam email, machine learning algorithms at the backend enable the system to identify and sort such messages automatically, placing it in the Spam folder and saving you the annoyance of doing it yourself while keeping you safe online. I often receive emails that claim I have won the lottery.

A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent. We treat this as an interpretable machine learning problem by leveraging the model that learned the low-dimensional representation to help identify the key differences between the groups. To solve this problem, we introduce a new type of explanation, a Global Counterfactual Explanation (GCE), and our algorithm, Transitive Global Translations (TGT), for computing GCEs. TGT identifies the differences between each pair of groups using compressed sensing but constrains those pairwise differences to be consistent among all of the groups. Empirically, we demonstrate that TGT is able to identify explanations that accurately explain the model while being relatively sparse, and that these explanations match real patterns in the data.

Optimal Transport (OT) distances are now routinely used as loss functions in ML tasks. Yet, computing OT distances between arbitrary (i.e. not necessarily discrete) probability distributions remains an open problem. This paper introduces a new online estimator of entropy-regularized OT distances between two such arbitrary distributions. It uses streams of samples from both distributions to iteratively enrich a non-parametric representation of the transportation plan. Compared to the classic Sinkhorn algorithm, our method leverages new samples at each iteration, which enables a consistent estimation of the true regularized OT distance. We cast our algorithm as a block-convex mirror descent in the space of positive distributions, and provide a theoretical analysis of its convergence. We numerically illustrate the performance of our method in comparison with concurrent approaches.

To effectively train Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a Mini-Batch Gradient Descent with Regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. It has demonstrated superior performances; however, there are also some limitations, e.g., it does not allow the user to specify the number of rules directly, and only Gaussian MFs can be used. This paper proposes two variants of MBGD-RDA to remedy these limitations, and show that they outperform the original MBGD-RDA and the classical ANFIS algorithms with the same number of rules. Furthermore, we also propose a rule pruning algorithm for TSK fuzzy systems, which can reduce the number of rules without significantly sacrificing the regression performance. Experiments showed that the rules obtained from pruning are generally better than training them from scratch directly, especially when Gaussian MFs are used.