. 2025 Feb 28;11:e2703. doi: 10.7717/peerj-cs.2703

Algorithm 5. AIFPIFSC1’s pseudocode algorithm.

Input:

(D_{t r a i n})_{m_{1} \times n}

C_{m_{1} \times 1}

, and

(D_{t e s t})_{m_{2} \times n}

Output:

T_{m_{2} \times 1}^{'}

1: function AIFPIFSC1(train, C, test)

λ_{1} \leftarrow r o u n d (\sqrt{n})

λ_{2} \leftarrow r o u n d (\ln (n) / \ln (l e n g t h (u n i q u e (C))))

4: Compute

i f w_{D_{t r a i n}}^{λ_{1} P}

using

D_{t r a i n}, C

and

λ_{1}

5: Compute feature fuzzification of D_train and D_test, namely

{\tilde{D}}_{t r a i n}

and

{\tilde{D}}_{t e s t}

6: Compute

{\tilde{\tilde{D}}}_{t r a i n}^{λ_{1}}

and

{\tilde{\tilde{D}}}_{t e s t}^{λ_{1}}

7: for i from 1 to tm do

8: Compute test ifpifs-matrix

[a_{i j}]

using

i f w_{D_{t r a i n}}^{λ_{1} P}

and

{\tilde{\tilde{D}}}_{k - t e s t}^{λ_{1}}

9: for j from 1 to em do

10: Compute train ifpifs-matrix

[b_{i j}]

using

i f w_{D_{t r a i n}}^{λ_{1} P}

and

{\tilde{\tilde{D}}}_{k - t r a i n}^{λ_{1}}

11:

c m (j, 1) \leftarrow {\tilde{s}}_{H} ([a_{i j}], [b_{i j}])

12:

c m (j, 2) \leftarrow {\tilde{s}}_{H s} ([a_{i j}], [b_{i j}])

13:

c m (j, 3) \leftarrow {\tilde{s}}_{C h} ([a_{i j}], [b_{i j}])

14:

c m (j, 4) \leftarrow {\tilde{s}}_{E} ([a_{i j}], [b_{i j}])

15:

c m (j, 5) \leftarrow {\tilde{s}}_{M}^{3} ([a_{i j}], [b_{i j}])

16:

c m (j, 6) \leftarrow {\tilde{s}}_{J} ([a_{i j}], [b_{i j}])

17:

c m (j, 7) \leftarrow {\tilde{s}}_{C} ([a_{i j}], [b_{i j}])

18: end for

19: for j from 1 to

s i z e (c m, 2)

20:

s d (1, j) \leftarrow s t d (c m (:, j))

21: end for

22:

w m 2 (1, :) \leftarrow 1 - N O R M A L I Z E (s d / 4)

23:

d m \leftarrow [w m 2; c m]

24:

i d m \leftarrow I N O R M A L I Z E (d m, λ_{2})

25:

[,,, o p] \leftarrow i s M B R 01 (i d m)

26:

t_{i 1}^{'} \leftarrow C (o p (1), 1)

27: end for

28: end function