H.3. Artificial Intelligence
F. Fadaei Noghani; M. Moattar
Abstract
Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost ...
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Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effective features, using an extended wrapper method, ensemble classification is performed. The extended feature selection approach includes a prior feature filtering and a wrapper approach using C4.5 decision tree. Ensemble classification, using cost sensitive decision trees is performed in a decision forest framework. A locally gathered fraud detection dataset is used to estimate the proposed method. The proposed method is assessed using accuracy, recall, and F-measure as evaluation metrics and compared with basic classification algorithms including ID3, J48, Naïve Bayes, Bayesian Network and NB tree. Experiments show that considering the F-measure as evaluation metric, the proposed approach yields 1.8 to 2.4 percent performance improvement compared to other classifiers.
H.6.4. Clustering
M. Lashkari; M. Moattar
Abstract
A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization ...
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A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages such as fast convergence rate, intelligent operators and simultaneous local and global search which are the motivations behind choosing this algorithm. In the Extended Cuckoo Algorithm, we have enhanced the operators in the classical version of the Cuckoo algorithm. The proposed operator of production of the initial population is based on a Chaos trail whereas in the classical version, it is based on randomized trail. Moreover, allocating the number of eggs to each cuckoo in the revised algorithm is done based on its fitness. Another improvement is in cuckoos’ migration which is performed with different deviation degrees. The proposed method is evaluated on several standard data sets at UCI database and its performance is compared with those of Black Hole (BH), Big Bang Big Crunch (BBBC), Cuckoo Search Algorithm (CSA), traditional Cuckoo Optimization Algorithm (COA) and K-means algorithm. The results obtained are compared in terms of purity degree, coefficient of variance, convergence rate and time complexity. The simulation results show that the proposed algorithm is capable of yielding the optimized solution with higher purity degree, faster convergence rate and stability in comparison to the other compared algorithms.
