Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Data Mining Approach to Solve the Goal Scoring Problem   In soccer, scoring goals is a fundamental objective which depends on many
conditions and constraints. Considering the RoboCup soccer 2D-simulator, this
paper presents a data mining-based decision system to identify the best time
and direction to kick the ball towards the goal to maximize the overall chances
of scoring during a simulated soccer match. Following the CRISP-DM methodology,
data for modeling were extracted from matches of major international
tournaments (10691 kicks), knowledge about soccer was embedded via
transformation of variables and a Multilayer Perceptron was used to estimate
the scoring chance. Experimental performance assessment to compare this
approach against previous LDA-based approach was conducted from 100 matches.
Several statistical metrics were used to analyze the performance of the system
and the results showed an increase of 7.7% in the number of kicks, producing an
overall increase of 78% in the number of goals scored.

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The categories of this paper are: stat
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The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
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The categories of this paper are: eess
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th