A Neuro-Fuzzy Rule-Based Classifier Using Important Features and Top Linguistic Features

Abstract

The efficient feature selection for predictive and accurate classification is highly desirable in many application domains. Most of the attempts to neuro-fuzzy classifier lose information to build interpretable neuro-fuzzy classification model. This paper proposes an interpretable neuro-fuzzy classification model with significant features without loss of knowledge, which is an extension of an existing interpretable neuro-fuzzy classification model. The proposed model is designed based on the consideration of feature importance that is determined by frequency of linguistic features. The rules are then made based on important features. Therefore, the knowledge acquired in network can be comprehended to logical rules using only important features. The proposed model finally performs classification task by rule-based approach. The average accuracy calculated by 10-fold cross validation finds that the proposed model can increase performance of the already proven neuro-fuzzy system for classification tasks.

A Framework for Applying CSFs to ERP Software Selection

“…The review studies indicate a variety of techniques used for ERP selection. The techniques that are primarily used are AHP (Wei, Chien, & Wang, 2005;Perera & Costa, 2008, Unal & Gunar, 2009, ANP (Ayag & Ozdemir, 2006;Yazgan, Boran & Goztepe, 2009;Lin, Chen & Ting, 2011), Fuzzy methods (Biswas, Bordoloi, Singh, & Purkayastha, 2016;Chen, Chen, Chen, Tsai & Lin, 2012;Grace & Williams, 2016) and few researches based on Data Envelopment Approach (Lall & Teyarachakul, 2006), Balance Scorecard (Cebeci, 2009), etc.…”

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

“…A variety of neuro-fuzzy methods are also proposed recently [15][16][17][18][19][20][21]. The applications of neurofuzzy methods include feature selection [19,21], classification [15][16][17][18][19][20], and image processing [17]. Furthermore, neuro-fuzzy methods using complex-valued inputs and outputs have been proposed and applied to image processing and time-series prediction [22,23].…”

“…An adaptive neuro-fuzzy system for building and optimizing fuzzy models has been proposed [14]. A variety of neuro-fuzzy methods are also proposed recently [15][16][17][18][19][20][21]. The applications of neurofuzzy methods include feature selection [19,21], classification [15][16][17][18][19][20], and image processing [17].…”

“…A variety of system structures and learning algorithms are available for neuro-fuzzy methods [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Learning of the classical neuro-fuzzy systems is based on the gradient descent method [9].…”