Recent advances in microarray technology allow scientists to measure expression levels of thousands of genes simultaneously in human tissue samples. This technology has been increasingly used in cancer research becaus...
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A number of factors and elements influence the introduction and long-term use of Information systems (IS) in organisations. Studies in long-term technology use indicate that influences that support users' decision...
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This paper investigates the applicability of Gaussian Processes (GP) classification for recognition of articulated and deformable human motions from image sequences. Using Tensor Subspace Analysis (TSA), space-time hu...
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Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been inve...
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ISBN:
(纸本)9781424429141
Decision tree classification is one of the most practical and effective methods which is used in inductive learning. Many different approaches, which are usually used for decision making and prediction, have been invented to construct decision tree classifiers. These approaches try to optimize parameters such as accuracy, speed of classification, size of constructed trees, learning speed, and the amount of used memory. There is a trade off between these parameters. That is to say that optimization of one may cause obstruction in the other, hence all existing approaches try to establish equilibrium. In this study, considering the effect of the whole data set on class assigning of any data, we propose a new approach to construct not perfectly accurate, but less complex trees in a short time, using small amount of memory. To achieve this purpose, a multi-step process has been used. We trace the training data set twice in any step, from the beginning to the end and vice versa, to extract the class pattern for attribute selection. Using the selected attribute, we make new branches in the tree. After making branches, the selected attribute and some records of training data set are deleted at the end of any step. This process continues alternatively in several steps for remaining data and attributes until the tree is completely constructed. In order to have an optimized tree the parameters which we use in this algorithm are optimized using genetic algorithms. In order to compare this new approach with previous ones we used some known data sets which have been used in different researches. This approach has been compared with others based on the classification accuracy and also the decision tree size. Experimental results show that it is efficient to use this approach particularly in cases of massive data sets, memory restrictions or short learning time.
In this paper, a novel variable rate based time frame scheduling scheme is proposed to further reduce collisions and improve energy saving in wireless sensor networks. The MAC combines CSMA and TDMA functionalities, w...
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Current Wi-Fi network infrastructure inherently lacks reliable positional knowledge of the origin of individual network packets. As a consequence, attackers are potentially able to impersonate legitimate Wi-Fi network...
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Ambiguity is a major problem of software errors because much of the requirements specification is written in a natural language format. Therefore, it is hard to identify consistencies because this format is too ambigu...
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We propose an architecture-based testing and reliability framework for mobile applications. During our literature study, we explored some of the software testing and reliability techniques available, as well as invest...
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Gene expression data are expected to be of significant help in the development of efficient cancer diagnosis and classification platforms. One problem arising from these data is how to select a small subset of genes f...
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Microarray device offers the ability to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that cou...
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