Two manufacturing applications of the fuzzy K-NN algorithm
References (30)
- et al.
Tool condition monitoring in turning using fuzzy set theory
Int. J. Machine Tools Manufact.
(1992) - et al.
Automatic recognition of welding defects in real-time radiography
NDT Int.
(1990) - et al.
An approach to monitoring of the grinding process using acoustic emission technique
Ann. CIRP
(1994) - et al.
Estimation of tool wear length in finish milling using a fuzzy inference algorithm
Wear
(1993) - et al.
Analytical modeling of acoustic emission for monitoring of peripheral milling process
Int. J. Machine Tools Manufact.
(1991) - et al.
A survey of automated visual inspection
Comput. Vision Image Understanding
(1995) Use of neural networks in nuclear power plants
ISA Trans.
(1993)- et al.
Acoustic emission for process control and monitoring of surface integrity during grinding
Ann. CIRP
(1994) Fuzzy sets
Inform. Control
(1965)Pattern Recognition with Fuzzy Objective Function Algorithms
(1981)
Pattern recognition in nondestructive evaluation of materials
Handbook of Pattern Recognition and Computer Vision
Acoustic emission sensing of tool wear in face milling
J. Eng. Ind.
An inexpensive system for classifying tool wear states using pattern recognition
Wear
In process recognition of cutting states
JAME Int. J. Ser. C
An investigation of grinding and wheel loading using acoustic emission
J. Eng. Ind.
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Interval–valued fuzzy and intuitionistic fuzzy–KNN for imbalanced data classification
2021, Expert Systems with ApplicationsGenders prediction from indoor customer paths by Levenshtein-based fuzzy kNN
2019, Expert Systems with ApplicationsCitation Excerpt :They showed that the developed fuzzy kNN (FkNN) algorithm reasonably assigns the membership values and produces lower error rates. Warren and Damin (1997) applied the fuzzy kNN in two manufacturing cases to identify welds from digital images and to determine failures in face milling operations. They obtained success rates with 93.2% and 89.1% by eliminating overlapped samples.
Evolutionary fuzzy k-nearest neighbors algorithm using interval-valued fuzzy sets
2016, Information SciencesCitation Excerpt :Fuzzy memberships enable fuzzy-kNN to achieve higher accuracy rates in most classification problems. This is also the reason why it has been the preferred choice in several applications in medicine [9,12], economy [11], bioinformatics [30], industry [33] and many other fields. The definition of fuzzy memberships is a fundamental issue in fuzzy-kNN.
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
2014, Information SciencesCitation Excerpt :Other recent applications are [17], in which diabetes diseases are diagnosed by incorporating FuzzyKNN into a full artificial immune recognition system, and [73], in which the joint use of particle swarm optimization, principal component analysis and FuzzyKNN is proposed for a thyroid disease diagnosis problem. Other medical technologies have also benefited from the use of fuzzy nearest neighbor classifiers: Liao et al. [67–69] presented several approaches to classifying radiographic images, including the use of feature extraction, Fuzzy C-Means clustering and FuzzyKNN. Another notable example is [65], in which Leszczynski et al. analyze the performance of FuzzyKNN with different classic distance measures (euclidean, mahalanobis, etc.) in a framework of decision making in radiotherapy.
An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach
2013, Expert Systems with ApplicationsCitation Excerpt :One unique characteristic of FKNN method is that it can assign a confidence degree for each predicted class. Thanks to its good properties, it has found its application in a wide range of classification tasks such as protein subcellular locations prediction (Huang & Li, 2004), protein solvent accessibility prediction (Sim, Kim, & Lee, 2005), hyperspectral satellite image classification (Yu, De Backer, & Scheunders, 2002), manufacturing applications (Warren Liao & Li, 1997), bankruptcy prediction (Chen et al., 2011a, 2011b), medical diagnosis (Liu et al., 2011; Seker, Odetayo, Petrovic, & Naguib, 2003) and so on. To the best of our knowledge, FKNN has not been examined for PD diagnosis although it has been used frequently for the classification of biological and medical data.
A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method
2011, Knowledge-Based SystemsCitation Excerpt :The class with the highest membership function value is taken as the winner. The FKNN method has been frequently used for the classification of biological data [28–30], image data [31,32] and so on. Nevertheless, only few works have paid attention to using FKNN to deal with the financial problems.