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Start Over Topic algorithms Topic computer science Language chinese
3 results

1. Decision Tree Algorithm Application for Classification in Data Mining Based on Extended SLIQ Algorithm.

Author

Zhang Wei

Subjects
ALGORITHMS, DATA mining, COMPUTATIONAL complexity, ARTIFICIAL neural networks, COMPUTER science
Abstract

This paper aims at improving the performance of the SLIQ decision tree algorithm (Mehta et. al, 1996) for classification in data mining. An improvement over the SLIQ algorithm has been proposed to reduce the computational complexity. It was observed that the decision tree constructed using the proposed decision tree algorithm gave far better classification accuracy than the classification accuracy obtained using the SLIQ algorithm and the neural network classification technique. [ABSTRACT FROM AUTHOR]

Published
2010

2. An algorithm to identify semanteme of GIS chinese inquiry sentences in surface layer.

Author

XU Ai-ping and OUYANG Hong-tao

Subjects
ALGORITHMS, COMPUTER algorithms, COMPUTER programming, SPACETIME, SEMANTIC computing, COMPUTER science
Abstract

Amied at the word meaning ambiguity in GIS Chinese inquiry system an algorithm to identify semanteme of GIS Chinese inquiry sentences in surface layer is present based on system dictionary, and it can solve the ambiguity successfully. A new data structure called "sentence link stack", is adopted in the algorithm to scan system dictionary and word dictionary respectively to confirm table and sentence link. The validity and complexity of time and space are tested. The experimental result shows that the validity can exceed 90% and the residue can be perfected in the sub - system of deep-layer semantinc analysis. The test data of space complexity shows that the link stack presented in this paper is more advanced than the stack structure used in references. The result of identification shows the algorithm is effective. [ABSTRACT FROM AUTHOR]

Published
2009

3. A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set

Author

Xu Dong, Zhang Ziying, Wang Xin, and Yulong Meng

Subjects
Current (mathematics), Discretization, Computer science, svm, General Engineering, 020206 networking & telecommunications, TL1-4050, 02 engineering and technology, Discretization algorithm, algorithms, variable precision rough set, Support vector machine, Nonlinear system, multiple dimensions, Bellman equation, Multiple time dimensions, breakpoint subset, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, discretization, Rough set, nonlinear systems, Algorithm, forest optimization network, Motor vehicles. Aeronautics. Astronautics
Abstract

Discretization of multidimensional attributes can improve the training speed and accuracy of machine learning algorithm. At present, the discretization algorithms perform at a lower level, and most of them are single attribute discretization algorithm, ignoring the potential association between attributes. Based on this, we proposed a discretization algorithm based on forest optimization and rough set (FORDA) in this paper. To solve the problem of discretization of multi-dimensional attributes, the algorithm designs the appropriate value function according to the variable precision rough set theory, and then constructs the forest optimization network and iteratively searches for the optimal subset of breakpoints. The experimental results on the UCI datasets show that:compared with the current mainstream discretization algorithms, the algorithm can avoid local optimization, significantly improve the classification accuracy of the SVM classifier, and its discretization performance is better, which verifies the effectiveness of the algorithm.

Published
2020