Table 2 Linear Diophantine fuzzy decision making.

From: Analyzing of optimal classifier selection for EEG signals of depression patients based on intelligent fuzzy decision support systems

Alternatives

\({G}_{1}\)

\({G}_{2}\)

\(\dots\)

\({G}_{m}\)

\({A}_{1}\)

\({\mathcal{l}}_{11}\)

\({\mathcal{l}}_{12}\)

\(\dots\)

\({\mathcal{l}}_{1m}\)

\({A}_{2}\)

\({\mathcal{l}}_{21}\)

\({\mathcal{l}}_{22}\)

\(\dots\)

\({\mathcal{l}}_{2m}\)

\({A}_{3}\)

\({\mathcal{l}}_{31}\)

\({\mathcal{l}}_{32}\)

\(\dots\)

\({\mathcal{l}}_{3m}\)

\(\vdots\)

\(\vdots\)

\(\vdots\)

\(\vdots\)

\(\vdots\)

\({A}_{n}\)

\({\mathcal{l}}_{n1}\)

\({\mathcal{l}}_{n2}\)

\(\dots\)

\({\mathcal{l}}_{nm}\)