Improving classification accuracy using automatically extracted training data

Classification is a core task in knowledge discovery and data mining, and there has been substantial research effort in developing sophisticated classification models. In a parallel thread, recent work from the NLP community suggests that for tasks such as natural language disambiguation even a simple algorithm can outperform a sophisticated one, if it is provided with large quantities of high quality training data. In those applications, training data occurs naturally in text corpora, and high quality training data sets running into billions of words have been reportedly used. We explore how we can apply the lessons from the NLP community to KDD tasks. Specifically, we investigate how to identify data sources that can yield training data at low cost and study whether the quantity of the automatically extracted training data can compensate for its lower quality. We carry out this investigation for the specific task of inferring whether a search query has commercial intent. We mine too...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Ariel Fuxman, Anitha Kannan, Andrew B. Goldberg, Rakesh Agrawal, Panayiotis Tsaparas, John C. Shafer
• Keywords: Data Mining, Extracted Training Data, KDD 2009, Quality Training Data, Training Data