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Imbalanced Learning with Parametric Linear Programming Support Vector Machine For Weather Data Application

Learning from imbalanced data sets is one of the aspects of predictive modeling and machine learning that has taken a lot of attention in the last decade. Multiple research projects have been carried out to adjust the existing algorithms for accurate predictions of both classes. The model proposed in this thesis is a linear Support Vector Machine model with L1-norm objective function with applications on weather data collected from the Bureau of Meteorology system in Australia. Apart from model selection and modifications we have also introduced a parametric modeling algorithm based on a novel parametric simplex approach for parameter tuning of Support Vector Machine. The combination of the two proposed approaches has yielded a significant improvement in predicting the minority class and decrease the model’s bias towards the majority class as is seen in most machine learning algorithms

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Fungi are one of the most important and diverse groups of organisms on the planet, having a dual impact on humanity. They adversely impact human and animal health and can be a scourge to agriculture, while in turn serving as a beneficial source for foods and beverages, new medications, and biocontrol. There are approximately 1.5 million different species of fungi on Earth, which largely reside in soil and plant. They are also readily found on human skin and within the gastrointestinal and genitourinary tract, yet only about 300 species are known to make people sick [1,2]. Fungi are bountiful in the environment and we encounter them everyday, usually in the form of freely dispersed spores and hyphal fragments that we breath-in. Typically, encounters with fungi are harmless, as the human immune systems is well poised to handle such interactions. However, some fungal species pose significant health risks, such as endemic mycoses or those producing toxins like mycotoxins. Most importantly, immune dysfunction can lead to serious life-threatening diseases or severe fungal-induced allergic diseases such as asthma or other chronic conditions [3]. In fact, most invasive fungal diseases are associated with changes in the host such as immunosuppression, antibiotic-mediated disruption of microflora, or other immunosuppressing conditions resulting from HIV/AIDS and hematologic malignancies [3,4]. Such diseases require therapy with antifungal agents. Yet, there are only limited classes available to treat invasive fungal infection, and emerging drug resistance further restricts treatment options. In some cases, agents used to control agriculturally important moulds are the same class as those used to treat humans, and de novo resistance can emerge from the environment [5]. Fungi are not always easy to detect and cryptic chronic infections in the form of unculturable organisms can confound diagnosis [6]. [...