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Stanislav Kirpichenko, Lev Utkin, Andrei Konstantinov and Vladimir Muliukha
A method for estimating the conditional average treatment effect under the condition of censored time-to-event data, called BENK (the Beran Estimator with Neural Kernels), is proposed. The main idea behind the method is to apply the Beran estimator for e...
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Andrei Konstantinov, Stanislav Kirpichenko and Lev Utkin
A new method for estimating the conditional average treatment effect is proposed in this paper. It is called TNW-CATE (the Trainable Nadaraya?Watson regression for CATE) and based on the assumption that the number of controls is rather large and the numb...
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Enrique Díaz de León-Hicks, Santiago Enrique Conant-Pablos, José Carlos Ortiz-Bayliss and Hugo Terashima-Marín
In the algorithm selection problem, where the task is to identify the most suitable solving technique for a particular situation, most methods used as performance mapping mechanisms have been relatively simple models such as logistic regression or neural...
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Anastasios Kaltsounis, Evangelos Spiliotis and Vassilios Assimakopoulos
We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for prod...
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Abdulaziz AlMohimeed, Hager Saleh, Sherif Mostafa, Redhwan M. A. Saad and Amira Samy Talaat
Cervical cancer affects more than half a million women worldwide each year and causes over 300,000 deaths. The main goals of this paper are to study the effect of applying feature selection methods with stacking models for the prediction of cervical canc...
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Nureni Ayofe Azeez, Oluwanifise Ebunoluwa Odufuwa, Sanjay Misra, Jonathan Oluranti and Robertas Dama?evicius
In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or ...
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