classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

Pattern recognition systems are widely used in a host of different fields. Due to some reasons such as lack of knowledge about a method based on which the best classifier is detected for any arbitrary problem, and thanks to significant improvement in accuracy, researchers turn to ensemble methods in almost every task of pattern recognition. Classification as a major task in pattern recognition,...

Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...

in this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. then, for optimizing the initial feature vector, a genetic algorithm is proposed. some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and k-nearest neig...

With an advance in technologies, different tumor features have been collected for Breast Cancer (BC) diagnosis, processing of dealing with large data set suffers some challenges which include high storage capacity and time require for accessing and processing. The objective of this paper is to classify BC based on the extracted tumor features. To extract useful information and diagnose the tumo...

In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neig...

conclusions in this study, we proposed a new computer aided diagnostic tool for the detection and classification of breast cancer. the obtained results showed that the proposed method is more reliable in diagnostic to assist the radiologists in the detection of abnormal data and to improve the diagnostic accuracy. results after classification with the ensemble supervised algorithm, the performa...

In this paper, a proposed ensemble classification for fully polarimetric synthetic aperture radar (PolSAR) data using a global-local classification approach is presented. In the first step, to perform the global classification, the training feature space is divided into a specified number of clusters. In the next step to carry out the local classification over each of these clusters, which cont...

Background and Objectives: Twitter Sentiment Classification is one of the most popular fields in information retrieval and text mining. Millions of people of the world intensity use social networks like Twitter. It supports users to publish tweets to tell what they are thinking about topics. There are numerous web sites built on the Internet presenting Twitter. The user can enter a sentiment ta...

Background: Breast cancer is the second leading cause of cancer death in women, after lung cancer. Due to the importance of predicting this disease, the use of data mining methods in medical research is more significant than before. Data mining algorithms can be a great help in preventing the development of lymphedema in patients. The aim Of this study was to create a diagnosis system that can ...