TY - JOUR

T1 - Spline optimization of soft connectives in machine learning models

AU - Кальницкий, Вячеслав Степанович

AU - Вилков, В.Б.

PY - 2024/4/28

Y1 - 2024/4/28

N2 - In this study, the problem of limited accuracy of machine learning models using soft logical connectives is investigated. Such connectives have shown their effectiveness in models with fuzzy initial data. On the one hand, the fundamental disadvantage of soft connectives is their non-associativity. On the other hand, the disadvantages of the currently used soft connectives include the loss of monotonicity and the inability to control several factors simultaneously. We minimized the difference between the connective and the associative connective, the latter in our study was the minimum function. In the resulting solution, the rate of deviation reduction is the highest among known connectives. We have achieved not only a small deviation from associativity, but also the presence of a large domain of exact associativity. This area is up to a third of the volume of all triples of arguments. A comparative analysis of the currently used soft connectives with the constructed model was carried out. It was shown that the spline approximation is able to reduce the influence of all negative factors and is more flexible in setting. Moreover, the constructed spline model allows numerous modifications depending on the factor that requires the most attention for different tasks.

AB - In this study, the problem of limited accuracy of machine learning models using soft logical connectives is investigated. Such connectives have shown their effectiveness in models with fuzzy initial data. On the one hand, the fundamental disadvantage of soft connectives is their non-associativity. On the other hand, the disadvantages of the currently used soft connectives include the loss of monotonicity and the inability to control several factors simultaneously. We minimized the difference between the connective and the associative connective, the latter in our study was the minimum function. In the resulting solution, the rate of deviation reduction is the highest among known connectives. We have achieved not only a small deviation from associativity, but also the presence of a large domain of exact associativity. This area is up to a third of the volume of all triples of arguments. A comparative analysis of the currently used soft connectives with the constructed model was carried out. It was shown that the spline approximation is able to reduce the influence of all negative factors and is more flexible in setting. Moreover, the constructed spline model allows numerous modifications depending on the factor that requires the most attention for different tasks.

KW - fuzzy sets

KW - soft connective

KW - soft signum

KW - spline

UR - https://www.mendeley.com/catalogue/633c3477-34fd-35d5-b585-86eb6c6ae3a1/

U2 - 10.19153/cleiej.27.1.2

DO - 10.19153/cleiej.27.1.2

M3 - Article

VL - 27

JO - CLEI Eletronic Journal (CLEIej)

JF - CLEI Eletronic Journal (CLEIej)

SN - 0717-5000

IS - 1

M1 - 2

ER -