Add to ORKGAbstract—A decision tree algorithm(ID3) may give good result when we process label data as input. In ID3 we give more data as input then it will give efficient result. With this method the complexity of ID3 is high. To reduce complexity we need to process less data as input and get efficient result. So for that we apply transduction to ID3. Here we use Naive Bayesian classifier to select the input data. By changing this method for selecting the input data can give better results than traditional ID3 when we compare accuracy as parameter Keywords—ID3; Naïve bayes; Transduction
The classification of large dimensional data sets arising from the merging of remote sensing data wi...
There is different decision tree based algorithms in data mining tools. These algorithms are used fo...
The objective of this thesis is to design a new classification-tree algorithm which will outperform ...
Abstract- Among decision tree classifiers, Bayesian classifiers, k-nearest-neighbor classifiers, cas...
Decision tree learning algorithm has been successfully used in expert systems in capturing knowledge...
Abstract — Data Mining is widening its scope by using new algorithms applicability in several domain...
It is often difficult for data miners to know which classifier will perform most effectively in any ...
The diversity and applicability of data mining are increasing day to day so need to extract hidden p...
Classifiers can be either linear means Naive Bayes classifier or non-linear means decision trees.In ...
Data Mining is the extraction of hidden predictive information from large database. Classification i...
Data mining is for new pattern to discover. Data mining is having major functionalities: classificat...
Decision tree is an important method in data mining to solve the classification problems. There are ...
Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classi-ca...
In machine learning, algorithms for inferring decision trees typically choose a single "best&qu...
The decision tree algorithm is a core technology in data classification mining, and ID3 (Iterative D...
The classification of large dimensional data sets arising from the merging of remote sensing data wi...
There is different decision tree based algorithms in data mining tools. These algorithms are used fo...
The objective of this thesis is to design a new classification-tree algorithm which will outperform ...
Abstract- Among decision tree classifiers, Bayesian classifiers, k-nearest-neighbor classifiers, cas...
Decision tree learning algorithm has been successfully used in expert systems in capturing knowledge...
Abstract — Data Mining is widening its scope by using new algorithms applicability in several domain...
It is often difficult for data miners to know which classifier will perform most effectively in any ...
The diversity and applicability of data mining are increasing day to day so need to extract hidden p...
Classifiers can be either linear means Naive Bayes classifier or non-linear means decision trees.In ...
Data Mining is the extraction of hidden predictive information from large database. Classification i...
Data mining is for new pattern to discover. Data mining is having major functionalities: classificat...
Decision tree is an important method in data mining to solve the classification problems. There are ...
Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classi-ca...
In machine learning, algorithms for inferring decision trees typically choose a single "best&qu...
The decision tree algorithm is a core technology in data classification mining, and ID3 (Iterative D...
The classification of large dimensional data sets arising from the merging of remote sensing data wi...
There is different decision tree based algorithms in data mining tools. These algorithms are used fo...
The objective of this thesis is to design a new classification-tree algorithm which will outperform ...