2011/08/02 by Vladimir Nikulin, Nikulin, Vladimir
Computer Science · Engineering · #Algorithms and Data Compression #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and Algorithms #Mineral Processing and Grinding
paper · pdf · doi:10.48550/arxiv.1108.0453
openalex publication_date 2011/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In many data mining applications collection of sufficiently large datasets is the most time consuming and expensive. On the other hand, industrial methods of data collection create huge databases, and make difficult direct applications of the advanced machine learning algorithms. To address the above problems, we consider active learning (AL), which may be very efficient either for the experimental design or for the data filtering. In this paper we demonstrate using the online evaluation opportunity provided by the AL Challenge that quite competitive results may be produced using a small percentage of the available data. Also, we present several alternative criteria, which may be useful for the evaluation of the active learning processes. The author of this paper attended special presentation in Barcelona, where results of the WCCI 2010 AL Challenge were discussed.