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Data generated by social media are frequently leveraged to build machine learning models that can accurately profile human behavior and sentiment. Twitter is a readily available source of population data that can be collected and used by any organization. Therefore, accurate machine learning models must be created to learn from this user-generated content. In this paper, we explore the task of classifying...
Bioinformatics datasets contain challenging characteristics, such as class imbalance that occurs when one class has many more instances than the other class(es). These challenges make the task of classification much more subtle for practitioners and researchers in the field. Fortunately, there are tools, such as ensemble learning and data sampling methods that can be applied to overcome these problems...
Noise is a prominent challenge found in many bioinformatics datasets and it refers to erroneous or missing data. The presence of noise in gene expression datasets has adverse effects on machine-learning techniques, such as supervised classification algorithms and feature selection techniques. Additionally, the identification of noise and its quantification are challenging tasks that require a proper...
Ensemble learning is a powerful tool that has shown promise when applied towards bioinformatics datasets. In particular, the Random Forest classifier has been an effective and popular algorithm due to its relatively good classification performance and its ease of use. However, Random Forest does not account for class imbalance which is known for decreasing classification performance and increasing...
An ever-growing number of users share their thoughts and experiences using the Twitter micro logging service. Although sometimes dismissed as containing too little content to convey significant information, these messages can be combined to build a larger picture of the user posting them. One particularly notable personality trait which can be discovered this way is psychopathy: the tendency for disregarding...
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