特征选择是模式识别系统的分类器设计之前一个重要而困难的一个课题。在目前现有的方法中,基于决策界的特征选择是其中一类方法。文中将覆盖算法应用于特征提取,提出了基于覆盖算法决策界的特征选择算法(Feature SelectionAlgorithm bas...特征选择是模式识别系统的分类器设计之前一个重要而困难的一个课题。在目前现有的方法中,基于决策界的特征选择是其中一类方法。文中将覆盖算法应用于特征提取,提出了基于覆盖算法决策界的特征选择算法(Feature SelectionAlgorithm based on the Decision Boundary of Covering Algorithm,简称FSACA法),然后将该算法应用于一个字符识别的实例并与其他算法比较。实验结果证明了FSACA法的可行性和有效性。展开更多
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is ...Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection(FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes(NB),instance based(IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers.展开更多
The digital images have been studied for image classification, enhancement, image compression and image segmentation purposes. In the present work, it is proposed to study the effects of feature selection algorithm on...The digital images have been studied for image classification, enhancement, image compression and image segmentation purposes. In the present work, it is proposed to study the effects of feature selection algorithm on the predictive classification accuracy of algorithms used for discriminating the different plant leaf images. The process involves extracting the important texture features from the digital images and then subjecting them to feature selection and further classification process. The leaf image features have been extracted by using Gabor texture features and these Gabor features are subjected to Random Forest feature selection algorithm for extracting important texture features. The four classification algorithms like K-Nearest Neighbour, J48, Classification and Regression Trees and Random Forest have been used for classification purpose. This study shows that there is a net improvement in the predictive classification accuracy values, when classification algorithms have been applied on selected features over the complete set of features.展开更多
文摘特征选择是模式识别系统的分类器设计之前一个重要而困难的一个课题。在目前现有的方法中,基于决策界的特征选择是其中一类方法。文中将覆盖算法应用于特征提取,提出了基于覆盖算法决策界的特征选择算法(Feature SelectionAlgorithm based on the Decision Boundary of Covering Algorithm,简称FSACA法),然后将该算法应用于一个字符识别的实例并与其他算法比较。实验结果证明了FSACA法的可行性和有效性。
文摘Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection(FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes(NB),instance based(IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers.
文摘The digital images have been studied for image classification, enhancement, image compression and image segmentation purposes. In the present work, it is proposed to study the effects of feature selection algorithm on the predictive classification accuracy of algorithms used for discriminating the different plant leaf images. The process involves extracting the important texture features from the digital images and then subjecting them to feature selection and further classification process. The leaf image features have been extracted by using Gabor texture features and these Gabor features are subjected to Random Forest feature selection algorithm for extracting important texture features. The four classification algorithms like K-Nearest Neighbour, J48, Classification and Regression Trees and Random Forest have been used for classification purpose. This study shows that there is a net improvement in the predictive classification accuracy values, when classification algorithms have been applied on selected features over the complete set of features.
