A New Efficient and Effective Fuzzy Modeling Method for Binary Classification

2013 ◽  
pp. 41-59
Author(s):  
T. Warren Liao
Keyword(s):  
Binary Classification ◽  
False Negative ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Modeling Method ◽  
Domain Space ◽  
Time To Build ◽  
Test Errors ◽  
Training Error ◽  
Lower Test

This paper presents a new fuzzy modeling method that can be classified as a grid partitioning method, in which the domain space is partitioned by the fuzzy equalization method one dimension at a time, followed by the computation of rule weights according to the max-min composition. Five datasets were selected for testing. Among them, three datasets are high-dimensional; for these datasets only selected features are used to control the model size. An enumerative method is used to determine the best combination of fuzzy terms for each variable. The performance of each fuzzy model is evaluated in terms of average test error, average false positive, average false negative, training error, and CPU time taken to build model. The results indicate that this method is best, because it produces the lowest average test errors and take less time to build fuzzy models. The average test errors vary greatly with model sizes. Generally large models produce lower test errors than small models regardless of the fuzzy modeling method used. However, the relationship is not monotonic. Therefore, effort must be made to determine which model is the best for a given dataset and a chosen fuzzy modeling method.

A New Efficient and Effective Fuzzy Modeling Method for Binary Classification

2011 ◽  
Vol 1 (1) ◽  
pp. 17-35 ◽  
Author(s):  
T. Warren Liao
Keyword(s):  
Binary Classification ◽  
False Negative ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Modeling Method ◽  
Time To Build ◽  
Test Errors ◽  
Training Error ◽  
Lower Test ◽  
Small Models

This paper presents a new fuzzy modeling method that can be classified as a grid partitioning method, in which the domain space is partitioned by the fuzzy equalization method one dimension at a time, followed by the computation of rule weights according to the max-min composition. Five datasets were selected for testing. Among them, three datasets are high-dimensional; for these datasets only selected features are used to control the model size. An enumerative method is used to determine the best combination of fuzzy terms for each variable. The performance of each fuzzy model is evaluated in terms of average test error, average false positive, average false negative, training error, and CPU time taken to build model. The results indicate that this method is best, because it produces the lowest average test errors and take less time to build fuzzy models. The average test errors vary greatly with model sizes. Generally large models produce lower test errors than small models regardless of the fuzzy modeling method used. However, the relationship is not monotonic. Therefore, effort must be made to determine which model is the best for a given dataset and a chosen fuzzy modeling method.

Fuzzy Modeling to Simulate Surface Roughness in CNC Turning of AISI 1045 Steel

2011 ◽  
Vol 486 ◽  
pp. 262-265
Author(s):  
Amit Kohli ◽  
Mudit Sood ◽  
Anhad Singh Chawla
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Process Parameters ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Cnc Machine ◽  
Model Based ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

The objective of the present work is to simulate surface roughness in Computer Numerical Controlled (CNC) machine by Fuzzy Modeling of AISI 1045 Steel. To develop the fuzzy model; cutting depth, feed rate and speed are taken as input process parameters. The predicted results are compared with reliable set of experimental data for the validation of fuzzy model. Based upon reliable set of experimental data by Response Surface Methodology twenty fuzzy controlled rules using triangular membership function are constructed. By intelligent model based design and control of CNC process parameters, we can enhance the product quality, decrease the product cost and maintain the competitive position of steel.

AEC Classifier: A Tree-Based Classifier with Error Control for Medical Disease Diagnosis and Other Applications

2021 ◽  
Vol 15 (02) ◽  
pp. 241-262
Author(s):  
Wasif Bokhari ◽  
Ajay Bansal
Keyword(s):  
Error Control ◽  
Binary Classification ◽  
False Negative ◽  
Disease Diagnosis ◽  
False Negatives ◽  
Medical Disease ◽  
Unique Nature ◽  
Comparable Accuracy ◽  
The Cost ◽  
Roc Score

In medical disease diagnosis, the cost of a false negative could greatly outweigh the cost of a false positive. This is because the former could cost a life, whereas the latter may only cause medical costs and stress to the patient. The unique nature of this problem highlights the need of asymmetric error control for binary classification applications. In this domain, traditional machine learning classifiers may not be ideal as they do not provide a way to control the number of false negatives below a certain threshold. This paper proposes a novel tree-based binary classification algorithm that can control the number of false negatives with a mathematical guarantee, based on Neyman–Pearson (NP) Lemma. This classifier is evaluated on the data obtained from different heart studies and it predicts the risk of cardiac disease, not only with comparable accuracy and AUC-ROC score but also with full control over the number of false negatives. The methodology used to construct this classifier can be expanded to many more use cases, not only in medical disease diagnosis but also beyond as shown from analysis on different diverse datasets.

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

scholarly journals Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy Model

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3673 ◽  
Author(s):  
Zhili Long ◽  
Ronghua He ◽  
Yuxiang He ◽  
Haoyao Chen ◽  
Zuohua Li
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
Mobile Robotics ◽  
Low Cost ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Fuzzy Classification ◽  
Classification Model ◽  
Service Robot ◽  
Ultrasonic Array

This paper presents a modeling approach to feature classification and environment mapping for indoor mobile robotics via a rotary ultrasonic array and fuzzy modeling. To compensate for the distance error detected by the ultrasonic sensor, a novel feature extraction approach termed “minimum distance of point” (MDP) is proposed to determine the accurate distance and location of target objects. A fuzzy model is established to recognize and classify the features of objects such as flat surfaces, corner, and cylinder. An environmental map is constructed for automated robot navigation based on this fuzzy classification, combined with a cluster algorithm and least-squares fitting. Firstly, the platform of the rotary ultrasonic array is established by using four low-cost ultrasonic sensors and a motor. Fundamental measurements, such as the distance of objects at different rotary angles and with different object materials, are carried out. Secondly, the MDP feature extraction algorithm is proposed to extract precise object locations. Compared with the conventional range of constant distance (RCD) method, the MDP method can compensate for errors in feature location and feature matching. With the data clustering algorithm, a range of ultrasonic distances is attained and used as the input dataset. The fuzzy classification model—including rules regarding data fuzzification, reasoning, and defuzzification—is established to effectively recognize and classify the object feature types. Finally, accurate environment mapping of a service robot, based on MDP and fuzzy modeling of the measurements from the ultrasonic array, is demonstrated. Experimentally, our present approach can realize environment mapping for mobile robotics with the advantages of acceptable accuracy and low cost.

A hybrid fuzzy modeling method for short-term load forecasting

2000 ◽  
Vol 51 (3-4) ◽  
pp. 221-232 ◽  
Author(s):  
P.A. Mastorocostas ◽  
J.B. Theocharis ◽  
S.J. Kiartzis ◽  
A.G. Bakirtzis
Keyword(s):  
Load Forecasting ◽  
Fuzzy Modeling ◽  
Modeling Method ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Hamed Kharrati ◽  
Sohrab Khanmohammadi ◽  
Witold Pedrycz ◽  
Ghasem Alizadeh
Keyword(s):  
Stability Analysis ◽  
Nonlinear Dynamical Systems ◽  
Fitness Function ◽  
Fuzzy Model ◽  
Taylor Series Expansion ◽  
Fuzzy Modeling ◽  
Lyapunov Theory ◽  
Membership Functions ◽  
Polynomial Fuzzy Systems ◽  
Nonlinear Plant

This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model-based (FMB) systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA) is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy modeling. To validate the model, a controller based on proposed polynomial fuzzy systems is designed and then applied to both original nonlinear plant and fuzzy model for comparison. Additionally, stability analysis for the proposed polynomial FMB control system is investigated employing Lyapunov theory and a sum of squares (SOS) approach. Moreover, the form of the membership functions is considered in stability analysis. The SOS-based stability conditions are attained using SOSTOOLS. Simulation results are also given to demonstrate the effectiveness of the proposed method.

Application of Fuzzy Logic and Fuzzy Optimization Techniques in Medical Image Processing

Biometrics ◽  
2017 ◽  
pp. 907-932 ◽  
Author(s):  
Niladri Sekhar Datta ◽  
Himadri Sekhar Dutta ◽  
Koushik Majumder
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Fuzzy Optimization ◽  
Fuzzy Model ◽  
Optimization Method ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Fuzzy Variables ◽  
Truth Value

Fuzzy logic deals with approximate rather than fixed and exact reasoning. Fuzzy variables may have a truth value that ranges in degree between 0 and 1; extended to handle the concept of partial truth where the truth value may range between completely true or completely false. This computational logic uses truth degrees as a mathematical model of the vagueness phenomenon while probability is a mathematical model of ignorance. A huge number of complex problems may be solve using Fuzzy logic specifically Fuzzy modeling and optimization method. Fuzzy modeling is the understanding of the problem and analysis of the Fuzzy information where the Fuzzy optimization solves Fuzzy model optimally using optimization techniques via membership functions. In this research article authors describe the Fuzzy rules and its application and the different types of well known problems solved by the Fuzzy optimization technique.

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