Atış tipi tahminleme probleminde sınıflandırma algoritmalarının karşılaştırılması

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" Atış tipi tahminleme probleminde sınıflandırma algoritmalarının karşılaştırılması "
Türkmen, Fatih Ergenç Bostanoğlu, Belgin

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1059014
Doc. No	:	TL58131
Main Entry	:	Türkmen, Fatih
Title & Author	:	Atış tipi tahminleme probleminde sınıflandırma algoritmalarının karşılaştırılması\ Türkmen, FatihErgenç Bostanoğlu, Belgin
College	:	Izmir Institute of Technology (Turkey)
Date	:	2020
Degree	:	Master's
student score	:	2020
Note	:	66 p.
Abstract	:	The dramatic increase in the use of IoT devices has been leading to a huge amount of valuable data to be discovered. The knowledge extraction from such a huge amount of data requires an organized scientific set of processes. This requirement has pointed out the importance of the data mining applications. As a major data mining application, classification is a supervised learning technique that requires a feature set and target class through the training process. For the training process, the key point is determining the appropriate feature set for the classification algorithm. The improvements in cutting-edge technologies such as high resolution camera systems have made extracting the insights about next pitch available. Consequently, pitch type prediction has been standing out as an important research topic. In order to predict next pitch type, existing researches mostly focus on pitcher profile, batter profile and previous pitch data in feature set. There is no study analyzing the effect of the zone information in the prediction of the next pitch type. Therefore, this study has analyzed the contribution of zone information in pitch type prediction. Our approach is that, we aimed to reveal the contribution of zones with the high strike low bat rates for pitch type decision in pitcher and batter player match up. This aim directed us to analyze the pitch type prediction problem for both zone-based and non-zone-based approaches so that we can exhibit how much zone information contributes to the problem through different classification algorithms.
Descriptor	:	Computer engineering
	:	Internet of Things
	:	Neural networks
Added Entry	:	Ergenç Bostanoğlu, Belgin
Added Entry	:	Izmir Institute of Technology (Turkey)