Use of Domain Knowledge and Feature Engineering in Helping AI to Play Hearthstone

Przybyszewski, Przemyslaw; Dziewiatkowski, Szymon; Jaszczur, Sebastian; Smiech, Mateusz; Szczuka, Marcin

doi:10.15439/2017f567

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…With more than 1,300 participating teams and several thousands of submitted solutions, they significantly contributed to solving important reallife challenges. They also provided us with a comprehensive survey on the state-of-the-art ML approaches in the related fields, such as time series forecasting [15], feature extraction [16], as well as prediction model ensembling [17].…”

Section: The History Of Knowledgepitmentioning

confidence: 99%

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

Janusz¹,

Ślęzak²

2022

Annals of Computer Science and Information Systems

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We discuss the benefits of integrating the Knowl-edgePit data science competition platform with the BrightBox technology aimed at diagnostics of machine learning models embedded within complex software systems. We briefly recall the history of international challenges held at KnowledgePit and we also discuss in what sense such technologies as BrightBox can be helpful during the post-challenge analysis. In particular, we show how to combine solutions submitted by the competition participants in order to obtain even more accurate predictions. The discussed functionalities are of significant importance for the sponsors and organizers of data science / machine learning online contests because they support adoption of submissions while designing ultimate solutions of real-world problems.

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Section: The History Of Knowledgepitmentioning

confidence: 99%

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

Janusz¹,

Ślęzak²

2022

Annals of Computer Science and Information Systems

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“…Feature engineering often proves to be a very costly process as it involves manual extraction of significant features from the dataset which becomes a tiresome job [16] when the dataset is huge. Furthermore, getting the relevant features involve a deep understanding of the relevant domain [17] for which classification is to be made. Most of the time, there is a considerable number of resources that are used to undergo feature engineering.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

Waqar

Dawood

et al. 2021

Scientific Programming

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Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

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Predicting Frags in Tactic Games at KnowledgePit.ai: ICME 2023 Grand Challenge Report

Janusz,

Ślęzak

2023

2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

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Use of Domain Knowledge and Feature Engineering in Helping AI to Play Hearthstone

Cited by 4 publications

References 3 publications

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

KnowledgePit Meets BrightBox: A Step Toward Insightful Investigation of the Results of Data Science Competitions

An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

Predicting Frags in Tactic Games at KnowledgePit.ai: ICME 2023 Grand Challenge Report

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