IPSec is a policy-driven security mechanism. How to react on the diversiyt of network security and quickly generate corresponding security policy is one of the core issues of IPSec. This article introduces the traditi...
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IPSec is a policy-driven security mechanism. How to react on the diversiyt of network security and quickly generate corresponding security policy is one of the core issues of IPSec. This article introduces the traditional IPSec security policy and demonstrates an improved mechanism of implementing the IPSec security policy. Secondly, it constructs the security policy model based on id3 algorithm by adding the policy engine components for enhancing efficiency and flexibility of security policy management for IPSec. Finally, through a case analysis, it describes the dynamic generation process of IPSec security policy.
id3 algorithm is a classical algorithm in decision tree algorithm,commonly used in data *** to that spatial information data has the characteristics of large capacity and diversity,when id3 algorithm does data mining ...
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id3 algorithm is a classical algorithm in decision tree algorithm,commonly used in data *** to that spatial information data has the characteristics of large capacity and diversity,when id3 algorithm does data mining for spatial information,in the process of generating decision tree,some nodes will appear special sample sets which the information gain of each classification attribute is 0 and values of result attribute are not unique ***,conventional implementation of id3 algorithm can't ensure the generation of decision *** on this,conventional implementation of id3 algorithm is improved,and the fault tolerance of algorithm implementation is *** algorithm implementation can do data mining for spatial data set.
In the issue of information security risk evaluation,the asset,the threat and the vulnerability are the three most important *** asset identification is a primary link of information security risk evaluation *** this ...
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In the issue of information security risk evaluation,the asset,the threat and the vulnerability are the three most important *** asset identification is a primary link of information security risk evaluation *** this paper,the decision tree algorithm was applied into the identification of information assets;the basic process of id3 algorithm are described;the data of information system property is classified by id3 algorithm;the decision tree was made;and the rules are extracted for providing basis of information asset recognition.
This paper adopts the id3 algorithm for mining hidden classification rules from mass students39; physical constitution evaluation and sports training result data. It is helpful for PE teacher on planning exercise pr...
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This paper adopts the id3 algorithm for mining hidden classification rules from mass students' physical constitution evaluation and sports training result data. It is helpful for PE teacher on planning exercise prescriptions toward college students with different physical constitution conditions by decision support. The algorithm generates a decision tree by choosing attributes with maximum information gain ratio for classification(Fig. 1). Such process involves a classification training set R(Table 1) towards original data, a information gain calculation according to overall evaluation of the physical constitution, separately investigate information gain ratio between physical constitution overall evaluation and each classification attribute, eliminating classification attribute ids which has no practical significance.
In decision tree learning, Iterative Dichotomiser version 3 (id3) is an algorithm used to generate a decision tree invented by Ross Quinlan. The conventional id3 algorithm has more preference to the multi-valued attri...
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ISBN:
(纸本)9781612848334
In decision tree learning, Iterative Dichotomiser version 3 (id3) is an algorithm used to generate a decision tree invented by Ross Quinlan. The conventional id3 algorithm has more preference to the multi-valued attributes than the actually necessary ones in selecting attributes. To solve the weak point, the improved id3 algorithm (AAid3) is proposed through introducing the Attribute Attention. In this way, the decision tree’s dependency on the multi-valued attributes is decreased. Simulation experimental results show that the AAid3 algorithm is superior to the id3 algorithm on the accuracy of classification, concision of decision tree, independency from multi-valued attributes.
Data mining involves an integration of techniques from multiple disciplines such as database technology, statistics, machine learning. The objective of this research undertaking was to explore the possible application...
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Data mining involves an integration of techniques from multiple disciplines such as database technology, statistics, machine learning. The objective of this research undertaking was to explore the possible application of data mining technology for mining training dataset. In this paper, Improved id3 and Tk NN Clustering decision making, classification with clustering method is used to build an effective decision making approach for capable performance. And also we proposed Improved id3 with Tk NN algorithm for best car market analysis. We have executed in Weka Tool with Java code. We analyzed the graphical performance analysis with Classes to Clusters evaluation, purchase, safety, luggage booting, persons(seating capacity), doors, maintenance and buying attri butes of customer's requirements for unacceptable/acceptable/good/very good ratings of a car to purchase.
Intrusion detection systems (idSs) have become a necessary component of computers and information security framework. idSs commonly deal with a large amount of data traffic and these data may contain redundant and uni...
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Intrusion detection systems (idSs) have become a necessary component of computers and information security framework. idSs commonly deal with a large amount of data traffic and these data may contain redundant and unimportant features. Choosing the best quality of features that represent all of the data and exclude the redundant features is a crucial topic in idSs. In this paper, a new combination approach based on the id3 algorithm and the bees algorithm (BA) is proposed to select the optimal subset of features for an idS. The BA is used to generate a subset of features, and the id3 algorithm is used as a classifier. The proposed model is applied on KDD Cup 99 dataset. The obtained results show that the feature subset generated by the proposed id3-BA gives a higher accuracy and detection rate with a lower false alarm rate when compared to the results obtained by using all features.
Backbone is a set of special variables of propositional formula, while it had important applications in 3-sat problems and SAT-based applications, so it is very important to solve the backbone efficiently. This paper ...
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ISBN:
(纸本)9781728166094
Backbone is a set of special variables of propositional formula, while it had important applications in 3-sat problems and SAT-based applications, so it is very important to solve the backbone efficiently. This paper proposed id3_backbone algorithm for computing backbones of proposition formulae, this is because the principle of id3 algorithm is to calculate the attribute information gain to determine the decision attribute, which can reduce the number of calls to the SAT solver. Experiments showed: id3_Backbone algorithm can guarantee 75% accuracy under the condition of improving the efficiency, it propose an effective method to solve the problem of backbone set.
Emails are the way to communicate over the Internet but this method of communication is bothersome by the Spam emails. Spam emails are the waste of memory, money, time and communication bandwidth. Thus, Spam emails ne...
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ISBN:
(纸本)9781538682166
Emails are the way to communicate over the Internet but this method of communication is bothersome by the Spam emails. Spam emails are the waste of memory, money, time and communication bandwidth. Thus, Spam emails needed to be identified and culminated. Hence, use of the id3 algorithm for making the decision trees and the Hidden Markov Model for calculating the probabilities of the events that may occur is used in this paper as a combination to identify the emails as Spam or ham. The model labels the emails as Spam or ham by calculating total probability of an email using all posteriorly classified words in emails and then supervising all processed emails by making their decision trees. For this purpose, an Enron dataset of 5172 emails is used that contains 2086 Spam and 2086 ham pre-classified emails. The experimental result on the given dataset shows that an accuracy of 89% is obtained on the Spam emails.
id3 algorithm as a classical decision tree algorithm has been used broadly for its simple idea, facile realization, effectiveness and efficiency. Furthermore, lots of related algorithms have been proposed to improve I...
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ISBN:
(纸本)7560323553
id3 algorithm as a classical decision tree algorithm has been used broadly for its simple idea, facile realization, effectiveness and efficiency. Furthermore, lots of related algorithms have been proposed to improve id3 on different aspects such as id4, C4.5 and so on. In this paper, we propose the improved classification algorithm by minsup and minconf based on id3 to decrease the data amount and reduce the impact of data with poor-quality. This improved algorithm introduces two new concepts,support of test attribute set to class' and 'rule confidence', which are used to improve the decision tree construction process by both prepruning and postpruning and ultimately to increase the efficiency and effectiveness of classification. Both theoretical analysis and test show that the improved algorithm avoids constructing a large decision tree with lots of branches which contains little information by reducing the size of data set during building process and pruning the useless rules from the built decision tree. It weakens the affect of poor quality data and produces a more appropriate decision tree finally.
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