Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifie...
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ISBN:
(纸本)9781509065387
Opinion mining is an interested area of research, which epitomize the customer reviews of a product or service and express whether the opinions are positive or negative. Various methods have been proposed as classifiers for opinion mining such as Naive Bayesian, and Support vector machine, these methods classify opinion without giving us the reasons about why the instance opinion is classified to certain class. Therefore, in our work, we investigate opinion mining of Arabic text at the document level, by applying decisiontrees classification classifier to have clear, understandable rule, also we apply parallel decision trees classifiers to have efficient results. We applied parallel decision trees on two Arabic corpus of text documents by using parallel implementation of RapidMiner tools. In case of applying parallel decision tree family on OCA we get the best results of accuracy (93.83%), f-measure (93.22) and consumed time 42 Sec at thread 4, one of the resulted rule is. In case of applying parallel decision tree family on BHA we get the best results of accuracy (90.63%), f-measure (82.29) and consumed time 219 Sec at thread 4, one of the resulted rule is.
This paper presents an optimized parallel decision tree model based on rough set theory, first the model divides global database into subsets, then using the intuitive classification ability of decisiontree to learn ...
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ISBN:
(纸本)9783540859833
This paper presents an optimized parallel decision tree model based on rough set theory, first the model divides global database into subsets, then using the intuitive classification ability of decisiontree to learn the rules in each subset, at last merge each subset's rule set to obtain the global rule set. In this model, with the uncertain information analysis method of rough set, the author presents a massive data segmentation method and using the Weighted Mean Roughness as a decisiontree method for attribute selection. This parallel data mining model with the best segmentation algorithm based on rough set can be well used in dealing with massive database.
Since the amount of information is rapidly growing, there is an overwhelming interest in efficient network computing systems including Grids, public-resource computing systems, P2P systems and Cloud computing. In this...
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ISBN:
(纸本)9783642043932
Since the amount of information is rapidly growing, there is an overwhelming interest in efficient network computing systems including Grids, public-resource computing systems, P2P systems and Cloud computing. In this paper we take a detailed look at the problem of modeling and optimization of network computing systems for parallel decision tree induction methods. Firstly, we present a comprehensive discussion Oil mentioned induction methods with a special focus on their parallel versions. Next, we propose a generic optimization model of a network computing system that can be used for distributed implementation of parallel decision trees. To illustrate our work we provide results of numerical experiments showing that the distributed approach enables significant improvement of the system throughput.
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