Credit card fraud detection using Naïve Bayes model based and KNN classifier
@article{Kiran2018CreditCF, title={Credit card fraud detection using Na{\"i}ve Bayes model based and KNN classifier}, author={Sai Kiran and Jyoti Guru and Rishabh Kumar and Naveen Kumar and Deepak Katariya and Maheshwar Pershad Sharma}, journal={International Journal of Advance Research, Ideas and Innovations in Technology}, year={2018}, volume={4}, pages={44-47} }
Machine Learning is the technology, in which algorithms which are capable of learning from previous cases and past experiences are designed. It is implemented using various algorithms which reiterate over the same data repeatedly to analyze the pattern of data. The techniques of data mining are no far behind and are widely used to extract data from large databases to discover some patterns making decisions. This paper presents the Naive Bayes improved K-Nearest Neighbor method (NBKNN) for Fraud…
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15 Citations
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References
SHOWING 1-10 OF 10 REFERENCES
Anomaly-based intrusion detection through K-means clustering and naives bayes classification
- Computer Science
- 2013
This work proposes an integrated machine learning algorithm across K-Means clustering and Naive Bayes Classifier called KMC+NBC to overcome the drawbacks of current intrusion detection methods.
A Direct Ensemble Classifier for Imbalanced Multiclass Learning
- Computer Science2012 4th Conference on Data Mining and Optimization (DMO)
- 2012
An ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed and a combiner method called OR-tree is used to combine the decisions obtained from the ensemble classifiers.
Ant system-based feature set partitioning algorithm for K-NN and LDA ensembles construction
- Computer Science
- 2015
Experimental results showed that the proposed ant system-based feature set partitioning algorithm in constructing k-nearest neighbor (k-NN) and linear discriminant analysis (LDA) ensembles has successfully constructed better classifier ensemble for k-NN and LDA.
Cost-sensitive learning methods for imbalanced data
- Computer ScienceThe 2010 International Joint Conference on Neural Networks (IJCNN)
- 2010
Two empirical methods that deal with class imbalance using both resampling and CSL are presented, one of which can reduce the misclassification costs, and the second can improve the classifier performance.
An Optimized Cost-Sensitive SVM for Imbalanced Data Learning
- Computer SciencePAKDD
- 2013
An effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive SVM directly to improve the performance of classification by simultaneously optimizing the best pair of feature subset, intrinsic parameters and misclassification cost parameters is presented.
A Positive-biased Nearest Neighbour Algorithm for Imbalanced Classification
- Computer SciencePAKDD
- 2013
A Positive-biased Nearest Neighbour (PNN) algorithm, where the local neighbourhood of query instances is dynamically formed and classification decision is carefully adjusted based on class distribution in the local neighbourhoods.
An Ensemble Sentiment Classification System of Twitter Data for Airline Services Analysis
- Computer Science2015 IEEE International Conference on Data Mining Workshop (ICDMW)
- 2015
An ensemble sentiment classification strategy was applied based on Majority Vote principle of multiple classification methods, including Naive Bayes, SVM, Bayesian Network, C4.5 Decision Tree and Random Forest algorithms, and shows that the proposed ensemble approach outperforms these individual classifiers in this airline service Twitter dataset.
Optimally Combining Classifiers Using Unlabeled Data
- Computer ScienceCOLT
- 2015
A worst-case analysis of aggregation of classifier ensembles for binary classification identifies cases where a weighted combination of the classifiers can perform significantly better than any single classifier.
Hybrid Ensemble of classifiers using voting
- Computer Science2015 International Conference on Green Computing and Internet of Things (ICGCIoT)
- 2015
A hybrid ensemble classifier is proposed that combines the representative algorithms of Instance based learner, Naïve Bayes Tree and Decision Tree Algorithms using voting methodology and is applied on 28 bench mark dataset.
Energy Efficiency (2017)
- 2017