Yes / As complex data becomes the norm, greater understanding of machine learning (ML) applications is needed for content marketers. Unstructured data, scattered across platforms in multiple forms, impedes performance and user experience. Automated classification offers a solution to this. We compare three state-of-the-art ML techniques for multilabel classification - Random Forest, K-Nearest Neighbor, and Neural Network - to automatically tag and classify online news articles. Neural Network performs the best, yielding an F1 Score of 70% and provides satisfactory cross-platform applicability on the same organisation's YouTube content. The developed model can automatically label 99.6% of the unlabelled website and 96.1% of the unlabelled YouTube content. Thus, we contribute to marketing literature via comparative evaluation of ML models for multilabel content classification, and cross-channel validation for a different type of content. Results suggest that organisations may optimise ML to auto-tag content across various platforms, opening avenues for aggregated analyses of content performance.

The supervised and semi-supervised machine learning paradigms hinge on the idea that the training data is labeled. The label quality is often brought into question, and problems related to noisy, inaccurate, or missing labels are studied. One of these is an interesting and prevalent problem in the semi-supervised classification area where only some positive labels are known. At the same time, the remaining and often the majority of the available data is unlabeled, i.e., there are no negative examples. Known as Positive-Unlabeled (PU) learning, this problem has been identified with increasing frequency across many disciplines, including but not limited to health science, biology, bioinformatics, geoscience, physics, business, and politics. Also, there are several closely related machine learning problems, such as cost-sensitive learning and mixture proportion estimation. This dissertation explores the PU learning problem from the perspective of density estimation and proposes a new modular method compatible with the relabeling framework that is common in PU learning literature. This approach is compared with two existing algorithms throughout the manuscript, one from a seminal work by Elkan and Noto and a current state-of-the-art algorithm by Ivanov. Furthermore, this thesis identifies two machine learning application domains that can benefit from PU learning approaches, which were not previously seen that way: predicting length of stay in hospitals and automatic music tagging. Experimental results with multiple synthetic and real-world datasets from different application domains validate the proposed approach. Accurately predicting the in-hospital length of stay (LOS) at the time of admission can positively impact healthcare metrics, particularly in novel response scenarios such as the Covid-19 pandemic. During the regular steady-state operation, traditional classification algorithms can be used for this purpose to inform planning and resource management. However, when there are sudden changes to the admission and patient statistics, such as during the onset of a pandemic, these approaches break down because reliable training data becomes available only gradually over time. This thesis demonstrates the effectiveness of PU learning approaches in such situations through experiments by simulating the positive-unlabeled scenario using two fully-labeled publicly available LOS datasets. Music auto-tagging systems are typically trained using tag labels provided by human listeners. In many cases, this labeling is weak, which means that the provided tags are valid for the associated tracks, but there can be tracks for which a tag would be valid but not present. This situation is analogous to PU learning with the additional complication of being a multi-label scenario. Experimental results on publicly available music datasets with tags representing three different labeling paradigms demonstrate the effectiveness of PU learning techniques in recovering the missing labels and improving auto-tagger performance. / Graduate

One of the many challenges of machine learning are systems for automatic tagging of music, the complexity of this issue in particular. These systems can be practically used in the content analysis of music or the sorting of music libraries. This thesis deals with the design, training, testing, and evaluation of artificial neural network architectures for automatic tagging of music. In the beginning, attention is paid to the setting of the theoretical foundation of this field. In the practical part of this thesis, 8 architectures of neural networks are designed (4 fully convolutional and 4 convolutional recurrent). These architectures are then trained using the MagnaTagATune Dataset and mel spectrogram. After training, these architectures are tested and evaluated. The best results are achieved by the four-layer convolutional recurrent neural network (CRNN4) with the ROC-AUC = 0.9046 ± 0.0016. As the next step of the practical part of this thesis, a completely new Last.fm Dataset 2020 is created. This dataset uses Last.fm and Spotify API for data acquisition and contains 100 tags and 122877 tracks. The most successful architectures are then trained, tested, and evaluated on this new dataset. The best results on this dataset are achieved by the six-layer fully convolutional neural network (FCNN6) with the ROC-AUC = 0.8590 ± 0.0011. Finally, a simple application is introduced as a concluding point of this thesis. This application is designed for testing individual neural network architectures on a user-inserted audio file. Overall results of this thesis are similar to other papers on the same topic, but this thesis brings several new findings and innovations. In terms of innovations, a significant reduction in the complexity of individual neural network architectures is achieved while maintaining similar results.