How to squeeze the most from your training data

In many cases, the acquisition of well-labelled training data is a huge hurdle for developing accurate prediction systems with supervised learning. At Love the Sales, we had the requirement to apply classification to the textual metadata of 2 million products (mostly fashion and homewares) into 1,000 different categories – represented in a hierarchy. In order to achieve this, we have architected a hierarchical tree of chained 2-class linear (Positive vs Negative) Support Vector Machines (LibSVM), each responsible for binary document classification of each hierarchical class. A key learning, is that the way in which these SVM's are structured can actually have a significant impact on how much training data has to be applied, for example, a naive approach would have been as follows: This approach requires that for every additional sub-category, two new SVM's be trained – for example, the addition of a new class for'Swimwear' would require an additional SVM under Men's and Women's – not to mention the potential complexity of adding a'Unisex' class at the top level. Overall, deep hierarchical structures can be too rigid to work with.