Standard machine learning techniques typically require ample training data in the form of labeled instances. In many situations it may be too tedious or costly to obtain sufficient labeled data for adequate classifier performance. However, in text classification, humans can easily guess the relevance of features, that is, words that are indicative of a topic, thereby enabling the classifier to focus its feature weights more appropriately in the absence of sufficient labeled data. We will describe an algorithm for tandem learning that begins with a couple of labeled instances, and then at each iteration recommends features and instances for a human to label. Tandem learning using an "oracle" results in much better performance than learning on only features or only instances. We find that humans can emulate the oracle to an extent that results in performance (accuracy) comparable to that of the oracle. Our unique experimental design helps factor out system error from human error, leading to a better understanding of when and why interactive feature selection works.
['Hema Raghavan', 'James Allan']
An interactive algorithm for asking and incorporating feature feedback into support vector machines