Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Performance Optimization of a Fuzzy Entropy based Feature Selection and
  Classification Framework   In this paper, based on a fuzzy entropy feature selection framework,
different methods have been implemented and compared to improve the key
components of the framework. Those methods include the combinations of three
ideal vector calculations, three maximal similarity classifiers and three fuzzy
entropy functions. Different feature removal orders based on the fuzzy entropy
values were also compared. The proposed method was evaluated on three publicly
available biomedical datasets. From the experiments, we concluded the optimized
combination of the ideal vector, similarity classifier and fuzzy entropy
function for feature selection. The optimized framework was also compared with
other six classical filter-based feature selection methods. The proposed method
was ranked as one of the top performers together with the Correlation and
ReliefF methods. More importantly, the proposed method achieved the most stable
performance for all three datasets when the features being gradually removed.
This indicates a better feature ranking performance than the other compared
methods.

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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga