TY - JOUR ID - jf:DAMI T1 - Efficient prediction algorithms for binary decomposition techniques A1 - Park, Sang-Hyeun A1 - Fürnkranz, Johannes JA - Data Mining and Knowledge Discovery Y1 - 2012 VL - 24 IS - 1 SP - 40 EP - 77 PB - Springer Netherlands SN - 1384-5810 UR - http://www.ke.tu-darmstadt.de/publications/papers/dami12.pdf M2 - doi: 10.1007/s10618-011-0219-9 KW - aggregation KW - binary decomposition KW - efficient decoding KW - efficient voting KW - multiclass classification KW - pairwise classification KW - ternary ECOC N2 - Binary decomposition methods transform multiclass learning problems into a series of two-class learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows super-linearly with the number of classes, we need efficient methods for computing the predictions. In this article, we discuss an efficient algorithm that queries only a dynamically determined subset of the trained classifiers, but still predicts the same classes that would have been predicted if all classifiers had been queried. The algorithm is first derived for the simple case of pairwise classification, and then generalized to arbitrary pairwise decompositions of the learning problem in the form of ternary error-correcting output codes under a variety of different code designs and decoding strategies. ER -