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Malicious URLs Detection Using Decision Tree Classifiers and Majority Voting Technique
Cybernetics and Information Technologies, 2018 Researchers all over the world have provided significant and effective solutions to detect malicious URLs. Still due to the ever changing nature of cyberattacks, there are many open issues.
Patil Dharmaraj R., Patil J. B.
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LazyBum: Decision Tree Learning Using Lazy Propositionalization [PDF]
, 2020 15 pages, 5 ...
Schouterden, Jonas +2 more
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Istanbul Business Research, 2021 Innovation levels and capacities of countries are two very important factors for competitiveness as well as the current Industrial 4.0 Revolution. In this context, capacity and level are relative concepts, with a great need for a common measurement ...
Merve Doğruel, Seniye Ümit Fırat
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Formal Verification of Input-Output Mappings of Tree Ensembles
, 2020 Recent advances in machine learning and artificial intelligence are now being considered in safety-critical autonomous systems where software defects may cause severe harm to humans and the environment. Design organizations in these domains are currently
Nadjm-Tehrani, Simin, Törnblom, John
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Effective Decision Tree Learning
International Journal of Computer Applications, 2013 Classification is a data analysis technique. The decision tree is one of the most popular classification algorithms in current use for data mining because it is more interpretable. Training data sets are not error free due to measurement errors in the data collection process. Traditional decision tree classifiers are constructed without considering any
B. Kumara Swamy Achari +2 more
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Decision Stream: Cultivating Deep Decision Trees
, 2017 Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability. Tree node splitting based on relevant feature selection is a key step of decision tree learning, at the same time ...
Ignatov, Andrey, Ignatov, Dmitry
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Multi-test Decision Tree and its Application to Microarray Data Classification [PDF]
, 2014 Objective: The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the ...
Armstrong +46 more
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Random Prism: An Alternative to Random Forests. [PDF]
, 2011 Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy.
Bramer, Max, Stahl, Frederic
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Tree retraining in the decision tree learning algorithm
IOP Conference Series: Materials Science and Engineering, 2021 Abstract Decision trees belong to the most effective classification methods. The main advantage of decision trees is a simple and user-friendly interpretation of the results obtained. But despite its well-known advantages the method has some disadvantages as well.
S A Mitrofanov, E S Semenkin
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Efficient algorithms for decision tree cross-validation
, 2001 Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead.
Blockeel, Hendrik, Struyf, Jan
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