عنوان

Atış tipi tahminleme probleminde sınıflandırma algoritmalarının karşılaştırılması

Number

TLpq2522829004

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Atış tipi tahminleme probleminde sınıflandırma algoritmalarının karşılaştırılması

General Material Designation

[Thesis]

First Statement of Responsibility

Türkmen, Fatih

Subsequent Statement of Responsibility

Ergenç Bostanoğlu, Belgin

Name of Publisher, Distributor, etc.

Izmir Institute of Technology (Turkey)

Date of Publication, Distribution, etc.

2020

Specific Material Designation and Extent of Item

66

Dissertation or thesis details and type of degree

Master's

Body granting the degree

Izmir Institute of Technology (Turkey)

Text preceding or following the note

2020

Text of Note

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.

Computer engineering

Internet of Things

Neural networks

Ergenç Bostanoğlu, Belgin

Türkmen, Fatih

Electronic name

p

[Thesis]

276903

a

Y