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X-TREPAN: a multi class regression and adapted extraction of comprehensible decision tree in artificial neural networks
In this work, the TREPAN algorithm is enhanced and extended for extracti...
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Predicting the Results of LTL Model Checking using Multiple Machine Learning Algorithms
In this paper, we study how to predict the results of LTL model checking...
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Crime Prediction Using Spatio-Temporal Data
A crime is a punishable offence that is harmful for an individual and hi...
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Predicting Eating Events in Free Living Individuals -- A Technical Report
This technical report records the experiments of applying multiple machi...
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Predictors of short-term decay of cell phone contacts in a large scale communication network
Under what conditions is an edge present in a social network at time t l...
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A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application
The aim of this work is to propose a meta-algorithm for automatic classi...
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Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
Top quarks are the most massive particle in the Standard Model and are p...
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PREDICTION OF DECAY MODES OF HIGGS BOSON USING CLASSIFICATION ALGORITHMS
Computational algorithms have been implied to various problems in particle physics from the explosion of applications in particles to event identification and reconstruction. The motive of this study is to propose a model that predicts the decay events of High Boson as “tau-tau decay of High Boson” or" the background noise" after applying some feature extraction on the CERN dataset. Therefore, eight algorithms namely “K” Nearest Neighbor, Artificial Neural Networks, Naïve Bayes algorithm, Logistic Regression algorithm, SVM, Random Forest Decision Tree, and Gradient Boosting are used in this experiment. The performances of all the algorithms are examined basis accuracy and computational time. Derived results show the decision tree outperforms the best on measures like Accuracy and Computational Time.
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