Improving decision tree performance by exception handling

Balamurugan, S.; Pramala, S.; Rajalakshmi, B.; Rajaram, R.

doi:10.1007/s11633-010-0517-5

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…The splitting process would be stopped if stopping rules are met, such as tree depth or minimum number of records in a leaf. Parameters that may influence the performance of a decision tree model include the maximum number of features used for splitting, the maximum depth of the decision tree, the minimum number of samples in a leaf node, and the minimum number of samples to split [53,54]. Among all the parameters, the minimum number of samples to split and the maximum depth of the decision tree are two of the most important that affect the performance of the model and should be seriously considered when adjusting parameters [55][56][57].…”

Section: Description Of Classifiersmentioning

confidence: 99%

A Machine Learning Approach to Crater Classification from Topographic Data

et al. 2019

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Craters contain important information on geological history and have been widely used for dating absolute age and reconstructing impact history. The impact process results in a lot of ejected fragments and these fragments may form secondary craters. Studies on distinguishing primary craters from secondary craters are helpful in improving the accuracy of crater dating. However, previous studies about distinguishing primary craters from secondary craters were either conducted by manual identification or used approaches mainly concerning crater spatial distribution, which are time-consuming or have low accuracy. This paper presents a machine learning approach to distinguish primary craters from secondary craters. First, samples used for training and testing were identified and unified. The whole dataset contained 1032 primary craters and 4041 secondary craters. Then, considering the differences between primary and secondary craters, features mainly related to crater shape, depth, and density were calculated. Finally, a random forest classifier was trained and tested. This approach showed a favorable performance. The accuracy and F1-score for fivefold cross-validation were 0.939 and 0.839, respectively. The proposed machine learning approach enables an automated method of distinguishing primary craters from secondary craters, which results in better performance.

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Section: Description Of Classifiersmentioning

confidence: 99%

A Machine Learning Approach to Crater Classification from Topographic Data

et al. 2019

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“…However, none of these researches, on the discovery of exceptions stated so far, use decision trees as the rule induction method. We have only found two research works [28], [16] that deal with exception handling while performing classification using a decision tree algorithm. These works modify the rule generation algorithm to resolve ties between classes that emerge at leaf node during the design process.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Decision Tree Algorithm for Discovering Intra and Inter Class Exceptions

Kumar¹,

Ratnoo²,

Vashishtha³

2019

IJITEE

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Decision tree algorithms, being accurate and comprehensible classifiers, have been one of the most widely used classifiers in data mining and machine learning. However, like many other classification algorithms, decision tree algorithms focus on extracting patterns with high generality and in the process, these ignore some rare but useful and interesting patterns that may exist in small disjuncts of data. Such extraordinary patterns with low support and high confidence capture very specific but exceptional behavior present in data. This paper proposes a novel Enhanced Decision Tree Algorithm for Discovering Intra and Inter-class Exceptions (EDTADE). Intra-class exceptions cover objects of unique interest within a class whereas inter-class exceptions capture rare conditions due to which we are forced shift the class of few unusual objects. For instance, whales and bats are examples of intra-class exceptions since these have unique characteristics within the class of mammals. Further, most of the birds are flying creatures, but the rare birds, like penguin and ostrich fall in the category of no flying birds. Here, penguin and ostrich are inter-class exceptions. In fact, without knowing about such exceptional patterns, our knowledge about a domain is incomplete. We have enhanced the decision tree algorithm by defining a framework for capturing intra and inter-class exceptions at leaf nodes of a decision tree. The proposed algorithm (EDTADE) is applied to many datasets from UCI Machine Learning Repository. The results show that the EDTADE has been successful in discovering many intra and inter-class exceptions. The decision tree augmented with intra and inter-class exceptions are more accurate, comprehensible as well as interesting since these provide additional knowledge in the form of exceptional patterns that deviate from the general rules discovered for classification

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