GA Tree: genetically evolved decision trees

Papagelis, Athanasios; Kalles, Dimitris

doi:10.1109/tai.2000.889871

Cited by 37 publications

(34 citation statements)

References 9 publications

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“…Some studies (e.g., [8]) estimated the similarity of different DTs using a simple formula based only on the differences between the number of nodes and tree levels. For example, in [27], the similarity of DTs was estimated using a simple formula: tree diff = |(levels tree1 -levels tree2) + (nodes tree1 -nodes tree2)| .…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, the similarities among DTs are investigated for tree matching problems, ensemble learning, interpreting DTs, and change detection in classification models. A number of recent studies needed to evaluate the similarity of DTs for different purposes in different application areas such as for privacy preserving [6], agent-based modeling systems [7], the construction of a genetic algorithm tree (GATree) [8], and speech recognition [9].…”

Section: Introductionmentioning

confidence: 99%

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DTreeSim: A new approach to compute decision tree similarity using re-mining

Bakırlı¹,

Birant²

2017

Turk J Elec Eng & Comp Sci

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A number of recent studies have used a decision tree approach as a data mining technique; some of them needed to evaluate the similarity of decision trees to compare the knowledge reflected in different trees or datasets. There have been multiple perspectives and multiple calculation techniques to measure the similarity of two decision trees, such as using a simple formula or an entropy measure. The main objective of this study is to compute the similarity of decision trees using data mining techniques. This study proposes DTreeSim, a new approach that applies multiple data mining techniques (classification, sequential pattern mining, and k-nearest neighbors) sequentially to identify similarities among decision trees. After the construction of decision trees from different data marts using a classification algorithm, sequential pattern mining was applied to the decision trees to obtain rules, and then the k-nearest neighbor algorithm was performed on these rules to compute similarities using two novel measures: general similarity and pieced similarity.Our experimental studies compared the results of these novel similarity measures and also compared our approach with existing approaches. Our comparisons indicate that our proposed approach performs better than existing approaches, because it takes into account the values of the branches in the trees through sequential pattern mining.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DTreeSim: A new approach to compute decision tree similarity using re-mining

Bakırlı¹,

Birant²

2017

Turk J Elec Eng & Comp Sci

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“…Papagelis and D. Kalles [10] proposed GATree, an algorithm for genetically evolving decision trees. The genetic algorithms use binary string as initial populations but GATree uses binary decision trees as initial populations.…”

Section: Decision Tree Learningmentioning

confidence: 99%

An Optimistic Data Mining Approach for Handling Large Data Set using Data Partitioning Techniques

Patil¹,

Bichkar²

2011

IJCA

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The use of the Internet for various purposes leads to collection of large volume of data. The knowledge contents of large data can be utilized to improve decision-making process of an organization. The knowledge discovery on this high volume data becomes very slow, as it has to be done serially on currently available terabyte plus data sets. In some cases, mining of large data set may become impossible due to limitations of processor and memory. The proposed algorithm is based on Tim Oates and Davis Jensen's [1] findings which state that increasing size of training data does not considerably increase classification accuracy of a classifier. The proposed algorithm also follows survival of the fittest principal used in genetic algorithm. The solution provides partitioning algorithm wherein decision trees can be learned on partitioned data that are disjoint subsets of a complete data set. These learned decision trees have comparable accuracies with each other and that is equivalent to the tree learned on complete data set. The algorithm finds a single tree with highest accuracy amongst the learned decision trees. The selected decision tree is used for classification of unseen data. The results on 12 benchmark data sets from UCI data repository indicate that the final learned decision tree have equal accuracy and in many cases, significant improvement in classification accuracy is observed, improvement in classification performance as compared to decision trees learned on the entire data set. An experiment on big data set Census-income (KDD) also supports the claim. The most important aspect of this approach is that it is very simple as compared to other methods with enhanced classification performance.

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“…While dealing with larger, potentially huge search space Genetic algorithms provide global search through space in many directions simultaneously, thereby improving the probability of finding the global optimum to obtain optimal combinations of things and solutions [14]- [16].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Multiple Imputation of Missing Data with Genetic Algorithm based Techniques

Patil¹,

Bichkar²

2010

IJCA

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ABSRACTMissing data is one of the major issues in data mining and pattern recognition. The knowledge contains in attributes with missing data values are important in improving decisionmaking process of an organization. The learning process on each instance is necessary as it may contain some exceptional knowledge. There are various methods to handle missing data in decision tree learning. The proposed imputation algorithm is based on the genetic algorithm that uses domain values for that attribute as pool of solutions. Survival of the fittest is the basis of genetic algorithm. The fitness function is classification accuracy of an instance with imputed value on the decision tree. The global search technique used in genetic algorithm is expected to help to get optimal solution.

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GA Tree: genetically evolved decision trees

Cited by 37 publications

References 9 publications

DTreeSim: A new approach to compute decision tree similarity using re-mining

DTreeSim: A new approach to compute decision tree similarity using re-mining

An Optimistic Data Mining Approach for Handling Large Data Set using Data Partitioning Techniques

Multiple Imputation of Missing Data with Genetic Algorithm based Techniques

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