Multiclass classifiers vs multiple binary classifiers using filters for feature selection

There are two classical approaches for dealing with multiple class data sets: a classifier that can deal directly with them, or alternatively, dividing the problem into multiple binary sub-problems. While studies on feature selection using the first approach are relatively frequent in scientific literature, very few studies employ the latter one. Out of the four classical methods that can be employed for generating binary problems from a multiple class data set (random, exhaustive, one-vs-one and one-vs-rest), the two last were employed in this work. Besides, four different methods were used for joining the results of these binary classifiers (sum, sum with threshold, Hamming distance and loss-based function). In this paper, both approaches (multiclass and multiple binary classifiers), are carried out using a combination method composed by a discretizer (two different were employed), a filter for feature selection (two methods were chosen), and a classifier (two classifiers were tested). The different combinations of the previous methods, with and without feature selection, were tested over 21 different multiple data sets. An exhaustive study of the results and a comparison between the described methods and some others on the literature is carried out.