Hierarchical Classification with Rare Categories and Inconsistencies




Naik, Azad

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Advancement in digital technology has generated a massive amount of data. Large amount of information streaming in from various sources such as phones, tablets, computers and internet has made an immense need to provide a structured and organized view of the data. Hierarchy (taxonomy) is one of the most easy and convenient way of data organization. It has been used extensively to store large volumes of data in various application domains ranging from biological datasets (for organizing genes and protein sequences) to image and text datasets (for providing the structured view of billions of images and web pages). Hierarchical structure representation of the data can be effectively used to eliminate the expensive and tedious task of manual classification. To this end, Hierarchical Classification (HC) deals with the task of automatically classifying the instances (examples) within the topic hierarchy have been developed. Although, HC is popular among the researchers due to its wide application, it faces severe challenges due to the following reasons:



Computer science, Engineering, Hierarchical Classification, Hierarchy (Taxonomy), Hybrid Prediction, Inconsistent hierarchy, Logistic Regression, Supervised Learning