Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An improved online learning algorithm for general fuzzy min-max neural
  network   This paper proposes an improved version of the current online learning
algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing
issues concerning expansion and contraction steps as well as the way of dealing
with unseen data located on decision boundaries. These drawbacks lower its
classification performance, so an improved algorithm is proposed in this study
to address the above limitations. The proposed approach does not use the
contraction process for overlapping hyperboxes, which is more likely to
increase the error rate as shown in the literature. The empirical results
indicated the improvement in the classification accuracy and stability of the
proposed method compared to the original version and other fuzzy min-max
classifiers. In order to reduce the sensitivity to the training samples
presentation order of this new on-line learning algorithm, a simple ensemble
method is also proposed.

22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph