Abstract
This work is focused on creating fuzzy granular classification models based on general type-2 fuzzy logic systems when consequents are represented by interval type-2 TSK linear functions. Due to the complexity of general type-2 TSK fuzzy logic systems, a hybrid learning approach is proposed, where the principle of justifiable granularity is heuristically used to define an amount of uncertainty in the system, which in turn is used to define the parameters in the interval type-2 TSK linear functions via a dual LSE algorithm. Multiple classification benchmark datasets were tested in order to assess the quality of the formed granular models; its performance is also compared against other common classification algorithms. Shown results conclude that classification performance in general is better than results obtained by other techniques, and in general, all achieved results, when averaged, have a better performance rate than compared techniques, demonstrating the stability of the proposed hybrid learning technique.
Highlights
Learning techniques in soft computing exist for the purpose of adjusting models so they can accurately represent data in some domain
Results were compared to Fuzzy C-Means (FCM) [26], Subtractive algorithm [33], Decision Trees, Support Vector
The work proposed in this paper is an initial exploration into the effectiveness of GT2 TSK fuzzy logic logic system system (FLS) for use in classification scenarios
Summary
Learning techniques in soft computing exist for the purpose of adjusting models so they can accurately represent data in some domain. By combining some of these direct approaches with each other or with other techniques, their performance can greatly improve, such that some steps can compensate performance loss or focus on optimizing a portion of the model Examples of such hybrid models are the scoring criterion for hybrid learning of two-component Bayesian multinets [5], hybrid learning particle swarm optimization with genetic disturbance intended to combat the problem of premature convergence observed in many particle swarm optimization variants [6], a hybrid Monte. Apart from Mamdani FLSs which represent consequents with membership functions, there exists the representation by linear functions These FLSs are named Takagi–Sugeno–Kang fuzzy logic systems (TSK FLSs). Some background is given which introduces the basic concepts used in the proposed hybrid learning technique; the proposed hybrid learning technique is thoroughly described; afterwards, some experimental data is given which defines the general performance of the technique; some concluding remarks are given
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