Pattern Analysis and Intelligent Systems
Somayeh Lotfi; Mohammad Ghasemzadeh; Mehran Mohsenzadeh; Mitra Mirzarezaee
Volume 7, Issue 1 , February 2021, , Pages 55-66
Abstract
The decision tree is one of the popular methods for learning and reasoning through recursive partitioning of data space. To choose the best attribute in the case on numerical features, partitioning criteria should be calculated for individual values or the value range of each attribute should be divided ...
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The decision tree is one of the popular methods for learning and reasoning through recursive partitioning of data space. To choose the best attribute in the case on numerical features, partitioning criteria should be calculated for individual values or the value range of each attribute should be divided into two or more intervals using a set of cut points. In partitioning range of attribute, the fuzzy partitioning can be used to reduce the noise sensitivity of data and to increase the stability of decision trees. Since the tree-building algorithms need to keep in main memory the whole training dataset, they have memory restrictions. In this paper, we present an algorithm that builds the fuzzy decision tree on the large dataset. In order to avoid storing the entire training dataset in main memory and overcome the memory limitation, the algorithm builds DTs in an incremental way. In the discretization stage, a fuzzy partition was generated on each continuous attribute based on fuzzy entropy. Then, in order to select the best feature for branches, two criteria, including fuzzy information gain and occurrence matrix are used. Besides, real datasets are used to evaluate the behavior of the algorithm in terms of classification accuracy, decision tree complexity, and execution time as well. The results show that proposed algorithm without a need to store the entire dataset in memory and reduce the complexity of the tree is able to overcome the memory limitation and making balance between accuracy and complexity .
Software Engineering and Information Systems
Majid Tajamolian; Mohammad Ghasemzadeh
Volume 5, Issue 4 , November 2019, , Pages 245-254
Abstract
Various numbering schemes are used to track different versions and revisions of files, software packages, and documents. One major challenge in this regard is the lack of an all-purpose, adaptive, comprehensive and efficient standard. To resolve the challenge, this article presents Quadruple Adaptive ...
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Various numbering schemes are used to track different versions and revisions of files, software packages, and documents. One major challenge in this regard is the lack of an all-purpose, adaptive, comprehensive and efficient standard. To resolve the challenge, this article presents Quadruple Adaptive Version Numbering Scheme. In the proposed scheme, the version identifier consists of four integers. These four numbers from Left to Right are called: "Release Sequence Number", "Generation Number", "Features List Number", and "Corrections List Number" respectively. In the article, special values are given for the quadruple numbers and their meanings are described. QAVNS is an "Adaptive" scheme; this means that it has the capability to track the different versions and revisions of files, software packages, project output documents, design documents, rules, manuals, style sheets, drawings, graphics, administrative and legal documents, and the other types of "Informational Objects" in different environments, without alterations in its structure. The proposed scheme has the capability to monitor changes in the types of informational objects, such as virtual machine memory, in the live migration process. The experimental and analytical results indicate the desirability and effectiveness of the proposed scheme in satisfying the desired expectations. The proposed scheme can become a common standard and successfully applied in all academic, engineering, administrative, legislative, legal, manufacturing, industrial, operational, software development, documentary, and other environments. The standardization of this scheme and its widespread usage can be a great help in improving everyone's understanding of the numbering of versions & revisions.