Modified Active Learning for Document Level Clustering
Snehal Patil, Prof. Jayant Jadhav · IARJSET · 2017
Learning to rank arises in many data mining applications, ranging from web search engine, online advertising to recommendation system.In learning to rank, the performance of a ranking model is strongly affected by the number of labeled examples in the training set; on the other hand, obtaining labeled examples for training data is very expensive and time-consuming.This presents a great need for the active learning approaches to select most informative examples for ranking learning; however, in the literature there is still very limited work to address active learning for ranking.In this paper, we propose a general active learning framework, expected loss optimization (ELO), for ranking.The ELO framework is applicable to a wide range of ranking functions.Under this framework, we derive a novel algorithm, expected discounted cumulative gain (DCG) loss optimization (ELO-DCG), to select most informative examples.Then, we investigate both query and document level active learning for raking and propose a two-stage ELO-DCG algorithm which incorporate both query and document selection into active learning.