Results 171 to 180 of about 31,619 (193)

A Comparative Analysis of Pruning Methods for C4.5 and Fuzzy C4.5

2015
The decision tree is an illustrious classification technique used for the pre-diction of the future data based on past experience. The decision tree is constructed using three major steps, first – constructing the decision tree to classify the data, second – pruning the decision tree to improve statistic certainty, third process the
Naseer Tayyeba, Asghar Sohail, Zhuang Yan, Fong Simon   +3 more
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Breast Cancer Using C4.5 Classification

INTERNATIONAL JOURNAL OF SCIENCE TECHNOLOGY AND HUMANITIES, 2015
Data mining is the process of discovering interesting knowledge from large amount of data stored in databases and these techniques based on advanced analytical methods and tools for handling a large amount of information. Much research is being carried out in applying data mining to a variety of applications in healthcare.
null Archana A, null Jayalakshmi R, null Balasubramanian T   +2 more
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C4.5 Data Algorithm for Industrialized Process

2021
Based on the problems of poor water temperature data quality, low prediction accuracy and poor stability in Wireless sensor for water quality monitoring based on Network, a water temperature prediction model for industrial aquaculture (ga-selm) based on genetic algorithm (GA) and improved extreme learning machine (Selm) was proposed.
Pin Ren, Xia Jiang
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Efficient C4.5 [classification algorithm]

IEEE Transactions on Knowledge and Data Engineering, 2002
We present an analytic evaluation of the runtime behavior of the C4.5 algorithm which highlights some efficiency improvements. Based on the analytic evaluation, we have implemented a more efficient version of the algorithm, called EC4.5. It improves on C4.5 by adopting the best among three strategies for computing the information gain of continuous ...
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An Evaluation of C4.5 and Fuzzy C4.5 with Effect of Pruning Methods

2015
Classification is a supervised learning technique in data mining classify historical data. The decision tree is easy method for inductive inference. The decision tree induction process has three major steps – first complete decision tree is constructed to classify all examples in the training data, the second is pruning this tree to decrease ...
Tayyeba Naseer, Sohail Asghar
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SSL-C4.5: Implementation of a Classification Algorithm for Semi-supervised Learning Based on C4.5

2020
Classification algorithms have been extensively studied in many of the major scientific investigations in recent decades. Many of these algorithms are designed for supervised learning, which requires labeled instances to achieve effective learning models.
Agustín Alejandro Ortiz-Díaz, Flavio Roberto Bayer, Fabiano Baldo   +2 more
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MReC4.5: C4.5 Ensemble Classification with MapReduce

2009 Fourth ChinaGrid Annual Conference, 2009
Classification is a significant technique in data mining research and applications. C4.5 is a widely used classification method, and ensemble learning adopts a parallel and distributed computing model for classification. Based on analyses of the MapReduce computing paradigm and the process of ensemble learning, we find that the parallel and distributed
Gongqing Wu, Haiguang Li, Xuegang Hu, Yuanjun Bi, Jing Zhang, Xindong Wu   +5 more
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HCV deduction of C4.5 rules

2022
This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.
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Diabetic Retinopathy Classification Using C4.5

2018
Early detection of diabetic retinopathy (DR) can prevent blindness and improve the quality of life. Practical detection requires a cost-effective screening over a large population. The presence of Microaneurysms (MAs) in a retinal image is the earliest sign of DR.
Mira Park, Peter Summons
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Statistical Entropy Measures in C4.5 Trees

International Journal of Data Warehousing and Mining, 2018
The main goal of this article is to present a statistical study of decision tree learning algorithms based on the measures of different parametric entropies. Partial empirical evidence is presented to support the conjecture that the parameter adjusting of different entropy measures might bias the classification.
Aldo Ramirez Arellano, Juan Bory-Reyes, Luis Manuel Hernandez-Simon   +2 more
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