Abstract In recent days, data growth is enormous and tough to handle it, in an oragnised manner and in apt time. In data mining, wide range of researches are available for managing data effectively. Document clustering is a spirited zone in data mining and here our main objective is to assemble the related documents. In this paper, we generate an algorithm... In recent days, data growth is enormous and tough to handle it, in an oragnised manner and in apt time. In data mining, wide range of researches are available for managing data effectively. Document clustering is a spirited zone in data mining and here our main objective is to assemble the related documents. In this paper, we generate an algorithm for clustering by means of Adaptive Pillar K-Means and Gaussian Firefly Algorithm. For determining the proper centroid in order to attain the proper clustered documents, Adaptive Pillar K-means algorithm is utilized. Subsequently, Gaussian firefly algorithm is exploited for the optimization process and also for enhancing the precision that results in reducing the sum of squared errors and computational time. Here, the performance of the proposed methodology is compared with various algorithms such as Genetic Algorithm, Ant colony optimization and gravity clustering. The attained results show the performance of the proposed methodology and the simulation results illustrated the betterment in quality with low sum of squared errors. Read More Read Less

Abstract One of the most important aspects of financial risk is credit risk management. Effective credit rating models are crucial for the credit institution in assessing credit applications, they have been widely studied in the field of statistics and machine learning. Given that small improvements in credit rating systems can generate significant profits, any improvement is of high interest to banks and... One of the most important aspects of financial risk is credit risk management. Effective credit rating models are crucial for the credit institution in assessing credit applications, they have been widely studied in the field of statistics and machine learning. Given that small improvements in credit rating systems can generate significant profits, any improvement is of high interest to banks and financial institutions. The ensemble methods are a set of algorithms whose individual decisions are combined to perform classification tasks. In this work, we propose an enhanced experimental comparative study of five ensemble methods associated with seven base classifiers using six public credit scoring datasets. Four popular evaluation metrics, including area under the curve (AUC), accuracy, false positive rate (FPR) and Time taken to build the model, are employed to measure the performance of models. The experimental results and statistical tests show that Pegasos model has a better overall performance than the other methods analyzed her for Boosting and Credal Decision Tree (CDT) model has a better overall performance than the other algorithms in the case of Bagging, Random Subspace, DECORATE and Rotation Forest. Read More Read Less

Abstract Many approaches in both feature extraction and classification have been proposed in order to build a robust automatic facial expression recognition system. The chosen features and classifiers are usually compared in limited scenarios. In this paper, 5 feature extractors (3 variations of Gabor filters, Local Binary Patterns, and Discrete Cosine Transform) and 4 classifiers (K-Nearest Neighbors, Support Vector Machine, Radial... Many approaches in both feature extraction and classification have been proposed in order to build a robust automatic facial expression recognition system. The chosen features and classifiers are usually compared in limited scenarios. In this paper, 5 feature extractors (3 variations of Gabor filters, Local Binary Patterns, and Discrete Cosine Transform) and 4 classifiers (K-Nearest Neighbors, Support Vector Machine, Radial Basis Function Neural Network, and Naive Bayes) were combined and applied to three different datasets: JAFFE, Yale, and CK+. The combinations of feature extractors and classifiers were compared in more robust settings, being evaluated in each dataset separately and in 3 cross-database settings, to verify the technique’s generalization power. All experiments were performed in two validation scenarios: in the first one, the system tries to recognize the facial expression of a person already known to it in the training phase, using an image not present in the training set (leave-one-out); in the other scenario, all images of a certain individual are in the testing set, so the system tries to recognize the facial expression of a person unknown to it in the training phase (leave-one-subject-out). Accuracy results, as well as computational times, are presented, suggesting the combination of feature extractors and classifiers more suited for generalization Read More Read Less