Paper Info
Title
Fractional Programming Weighted Decoding for Error-Correcting Output Codes
Abstract
In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach's Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments.
Year
DOI
Venue
2015
10.1007/978-3-319-20248-8_4
Lecture Notes in Computer Science
Field
DocType
Volume
Sequential decoding,Berlekamp–Welch algorithm,Error floor,Computer science,Algorithm,Decoding methods,List decoding,Classifier (linguistics),Fractional programming
Conference
9132
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
9
5
Name
Order
Citations
PageRank
Firat Ismailoglu131.74
Ida G. Sprinkhuizen-kuyper28413.83
Evgueni N. Smirnov32420.38
Sergio Escalera41415113.31
Ralf L. M. Peeters56222.61