OMCOKE: A Machine Learning Outlier-based Overlapping Clustering Technique for Multi-Label Data Analysis

Abstract

Clustering is one of the challenging machine learning techniques due to its unsupervised learning nature. While many clustering algorithms constrain objects to single clusters, K-means overlapping partitioning clustering methods assign objects to multiple clusters by relaxing the constraints and allowing objects to belong to more than one cluster to better fit hidden structures in the data. However, when datasets contain outliers, they can significantly influence the mean distance of the data objects to their respective clusters, which is a drawback. Therefore, most researchers address this problem by simply removing the outliers. This can be problematic especially in applications such as fraud detection or cybersecurity attacks risk analysis. In this study, an alternative solution to this problem is proposed that captures outliers and stores them on-the-fly within a new cluster, instead of discarding. The new algorithm is named Outlier-based Multi-Cluster Overlapping K-Means Extension (OMCOKE). Empirical results on real-life multi-label datasets were derived to compare OMCOKE's performance with other common overlapping clustering techniques. The results show that OMCOKE produced a better precision rate compared to the considered clustering algorithms. This method can benefit various stakeholders as these outliers could have real-life applications in cybersecurity, fraud detection, and the anti-phishing of websites.Povzetek: V tej študiji je predlagana alternativna rešitev (OMCOKE), ki zajame izstope in jih sproti shrani v novo gručo, namesto da bi jih odstranila.

A Multichannel Convolutional Neural Network for Multilabel Sentiment Classification Using Abilify Oral User Reviews

“…Specifically, we fixed the following hyperparameters using the random approach such as input length with 150 units, 100 embedding dimension, three kernel sizes (4, 6, and 8), ReLU activation, 0.8 dropouts, pooling size 2, 10 units in the fully connected layer, 20 epochs, and Adam optimizer with a binary cross-entropy loss function. The proposed multichannel CNN model for multilabel classification is evaluated using various multilabel metrics, namely, accuracy or exact match, hamming loss, F1-micro average score, and accuracy per label [3,5,20,21]. Table 2 shows the performance of the proposed multichannel CNN model for multilabel classification.…”