A novel Neuro-fuzzy classification technique for data mining

Ghosh, Soumadip; Biswas, Sushanta; Sarkar, Debasree; Sarkar, Partha Pratim

doi:10.1016/j.eij.2014.08.001

Cited by 56 publications

(32 citation statements)

References 17 publications

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“…Ghosh et al [17] studied about a new Neuro-fuzzy classification approach used for data mining. The data provided to the Neuro-fuzzy system underwent Fuzzification by using the generalized bell-structured membership function.…”

Section: Pramoda Patro Krishna Kumar G Suresh Kumarmentioning

confidence: 99%

Various Classifiers Performance Based Machine Learning Methods

2019

IJRTE

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Classification is a form of data mining (regarding machine learning) approach that is helpful in the prediction of group membership for data instances, where the data input is used by the computer program for learning and thereafter this learning is used for classifying the fresh observation made. This data set might just be bi-class or it can be multi-class also. Few instances of the problems in classification include: speech identification, handwriting identification, bio metric detection, document classification etc. Many classification methods exist, which can be utilized for classification. In this research work, the fundamental classification approaches and few important kinds of classification approaches that include decision tree induction, Bayesian networks,k-nearest neighbor classifier and Support Vector Machines (SVM) and fuzzy learning classifiers with their merits, drawbacks, probable applications and challenges faced with the solution available. There are different problems that have an effect on the classification and prediction. The objective of this research work is to render an extensive review of various classification approaches in machine learning. At last, the future work intended on the best classification techniques for the input data are discussed.

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Section: Pramoda Patro Krishna Kumar G Suresh Kumarmentioning

confidence: 99%

Various Classifiers Performance Based Machine Learning Methods

2019

IJRTE

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“…The experimental results of their analysis classification techniques for mining showed that their approach improved identical dissemination of instances in ICDDR,B Hospital Surveillance data. each class Ghosh et al (2014). A novel Neuro-fuzzy Used a neuro-fuzzy data mining approach for classification of generalized bellclassification technique for data mining shaped membership functions.…”

Section: Malware Detection Based On Api Call Graphmentioning

confidence: 99%

Survey on Representation Techniques for Malware Detection System

Mohamed

Ithnin

2017

American Journal of Applied Sciences

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Malicious programs are malignant software's designed by hackers or cyber offenders with a harmful intent to disrupt computer operation. In various researches, we found that the balance between designing an accurate architecture that can detect the malware and track several advanced techniques that malware creators apply to get variants of malware are always a difficult line. Hence the study of malware detection techniques has become more important and challenging within the security field. This review paper provides a detailed discussion and full reviews for various types of malware, malware detection techniques, various researches on them, malware analysis methods and different dynamic programmingbased tools that could be used to represent the malware sampled. We have provided a comprehensive bibliography in malware detection, its techniques and analysis methods for malware researchers.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Hata¹,

Akhand²,

Islam³

et al. 2018

IJISA

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Abstract-The conventional real-valued neuro-fuzzy method (RNF) is based on classic fuzzy systems with antecedent membership functions and consequent singletons. Rules in RNF are made by all the combinations of membership functions; thus, the number of rules as well as total parameters increase rapidly with the number of inputs. Although network parameters are relatively less in the recently developed complex-valued neuro-fuzzy (CVNF) and quaternion neuro-fuzzy (QNF), parameters increase with number of inputs. This study investigates simplified fuzzy rules that constrain rapid increment of rules with inputs; and proposed simplified RNF (SRNF), simplified CVNF (SCVNF) and simplified QNF (SQNF) employing the proposed simplified fuzzy rules in conventional methods. The proposed simplified neuro-fuzzy learning methods differ from the conventional methods in their fuzzy rule structures. The methods tune fuzzy rules based on the gradient descent method. The number of rules in these methods are equal to the number of divisions of input space; and hence they require significantly less number of parameters to be tuned. The proposed methods are tested on function approximations and classification problems. They exhibit much less execution time than the conventional counterparts with equivalent accuracy. Due to less number of parameters, the proposed methods can be utilized for the problems (e.g., real-time control of large systems) where the conventional methods are difficult to apply due to time constrain.

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A novel Neuro-fuzzy classification technique for data mining

Cited by 56 publications

References 17 publications

Various Classifiers Performance Based Machine Learning Methods

Various Classifiers Performance Based Machine Learning Methods

Survey on Representation Techniques for Malware Detection System

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

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