Predictive machine-learning models based on neural networks are extremely powerful when judging large data sets. But understanding them is notoriously difficult. Neural networks are trained using labeled data sets. How well they perform is validated using a labeled test set. This is where model accuracy, confusion matrices, ROCs, etc. come in handy.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing machines and processes. To fully utilize this potential of dynamic manufacturing through automatic production planning, formal skill descriptions of the machines are essential. However, generating those skill descriptions in a manual fashion is labor-intensive and requires extensive domain-knowledge. In this contribution an ontology-based semi-automatic skill description system that utilizes production logs and industrial ontologies through inductive logic programming is introduced and benefits and drawbacks of the proposed solution are evaluated.

A simple and interpretable way to learn a dynamical system from data is to interpolate its vector-field with a kernel. In particular, this strategy is highly efficient (both in terms of accuracy and complexity) when the kernel is data-adapted using Kernel Flows (KF)~\cite{Owhadi19} (which uses gradient-based optimization to learn a kernel based on the premise that a kernel is good if there is no significant loss in accuracy if half of the data is used for interpolation). Despite its previous successes, this strategy (based on interpolating the vector field driving the dynamical system) breaks down when the observed time series is not regularly sampled in time. In this work, we propose to address this problem by directly approximating the vector field of the dynamical system by incorporating time differences between observations in the (KF) data-adapted kernels. We compare our approach with the classical one over different benchmark dynamical systems and show that it significantly improves the forecasting accuracy while remaining simple, fast, and robust.

Comparing prediction methods to define which one should be used for the task at hand is a daily activity for most data scientists. Usually, one will have a pool of classification models and will validate them using cross-validation to define which one is best. Another goal, however, is not to compare classifiers, but the learning algorithms themselves. The idea is: given this task (data), which learning algorithm (KNN, SVM, Random Forests, etc) will generate more accurate classifiers on a dataset of size D? As we will see, every method presented here has some pros and cons. However, the first intuition of using a two proportions test can lead to some really bad results.

Parkinson's disease (PD) is debilitating, progressive, and clinically marked by motor symptoms. As the second most common neurodegenerative disease in the world, it affects over 10 million lives globally. Existing diagnoses methods have limitations, such as the expense of visiting doctors and the challenge of automated early detection, considering that behavioral differences in patients and healthy individuals are often indistinguishable in the early stages. However, micrographia, a handwriting disorder that leads to abnormally small handwriting, tremors, dystonia, and slow movement in the hands and fingers, is commonly observed in the early stages of PD. In this work, we apply machine learning techniques to extract signs of micrographia from drawing samples gathered from two open-source datasets and achieve a predictive accuracy of 94%. This work also sets the foundations for a publicly available and user-friendly web portal that anyone with access to a pen, printer, and phone can use for early PD detection.

We study fairness in the context of classification where the performance is measured by the area under the curve (AUC) of the receiver operating characteristic. AUC is commonly used when both Type I (false positive) and Type II (false negative) errors are important. However, the same classifier can have significantly varying AUCs for different protected groups and, in real-world applications, it is often desirable to reduce such cross-group differences. We address the problem of how to select additional features to most greatly improve AUC for the disadvantaged group. Our results establish that the unconditional variance of features does not inform us about AUC fairness but class-conditional variance does. Using this connection, we develop a novel approach, fairAUC, based on feature augmentation (adding features) to mitigate bias between identifiable groups. We evaluate fairAUC on synthetic and real-world (COMPAS) datasets and find that it significantly improves AUC for the disadvantaged group relative to benchmarks maximizing overall AUC and minimizing bias between groups.

Imbalanced Learning (IL) is an important problem that widely exists in data mining applications. Typical IL methods utilize intuitive class-wise resampling or reweighting to directly balance the training set. However, some recent research efforts in specific domains show that class-imbalanced learning can be achieved without class-wise manipulation. This prompts us to think about the relationship between the two different IL strategies and the nature of the class imbalance. Fundamentally, they correspond to two essential imbalances that exist in IL: the difference in quantity between examples from different classes as well as between easy and hard examples within a single class, i.e., inter-class and intra-class imbalance. Existing works fail to explicitly take both imbalances into account and thus suffer from suboptimal performance. In light of this, we present Duple-Balanced Ensemble, namely DUBE , a versatile ensemble learning framework. Unlike prevailing methods, DUBE directly performs inter-class and intra-class balancing without relying on heavy distance-based computation, which allows it to achieve competitive performance while being computationally efficient. We also present a detailed discussion and analysis about the pros and cons of different inter/intra-class balancing strategies based on DUBE . Extensive experiments validate the effectiveness of the proposed method. Code and examples are available at https://github.com/ICDE2022Sub/duplebalance.

Neural networks leverage both causal and correlation-based relationships in data to learn models that optimize a given performance criterion, such as classification accuracy. This results in learned models that may not necessarily reflect the true causal relationships between input and output. When domain priors of causal relationships are available at the time of training, it is essential that a neural network model maintains these relationships as causal, even as it learns to optimize the performance criterion. We propose a causal regularization method that can incorporate such causal domain priors into the network and which supports both direct and total causal effects. We show that this approach can generalize to various kinds of specifications of causal priors, including monotonicity of causal effect of a given input feature or removing a certain influence for purposes of fairness. Our experiments on eleven benchmark datasets show the usefulness of this approach in regularizing a learned neural network model to maintain desired causal effects. On most datasets, domain-prior consistent models can be obtained without compromising on accuracy.

Not suitable for Time Series data: For Time Series data the order of the samples matter. But in Stratified Cross-Validation, samples are selected in random order. LeavePOut cross-validation is an exhaustive cross-validation technique, in which p-samples are used as the validation set and remaining n-p samples are used as the training set. Suppose we have 100 samples in the dataset. If we use p 10 then in each iteration 10 values will be used as a validation set and the remaining 90 samples as the training set. This process is repeated till the whole dataset gets divided on the validation set of p-samples and n-p training samples. All the data samples get used as both training and validation samples.