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首页> 外文期刊>International journal of computer science and network security >Performance Evaluation and Comparison of Classification Techniques for Outcome Estimation in Strategic Board Games
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Performance Evaluation and Comparison of Classification Techniques for Outcome Estimation in Strategic Board Games

机译：战略棋类游戏绩效评估的绩效评估和分类技术的比较

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摘要

Supervised learning aims to construct a distribution model for class labels with respect to features of prediction. Various machine learning approaches have been developed to analyze classification technique on different kinds of data. The objective of this work is to evaluate and compare the prediction performance of various classification techniques on 3 datasets belonging to strategic board games. This comparison analysis is done by using WEKA, open source software, which is responsible for implementing variety of machine learning algorithms for data-mining application i.e. classification. This work provides basic overview of selected machine learning classification models alongwith a brief description of datasets of three strategic board games. Then, it evaluates and compares the prediction performance of various classifiers using K-fold cross validation test mode. The results are based on several evaluation metrics like accuracy, precision, recall, kappa statistics, mean absolute error, and root mean squared error. Finally, it provides the best classification method for outcome prediction in strategic board games. Tree based LMT, SVM based SMO and K-NN based LBk are observed as the most suitable models for outcome prediction of strategic board games, LMT being the most influential one.
机译：监督学习的目的是针对预测特征构建类别标签的分布模型。已经开发了各种机器学习方法来分析不同种类的数据上的分类技术。这项工作的目的是评估和比较各种分类技术对属于战略棋盘游戏的3个数据集的预测性能。这种比较分析是通过使用开放源代码软件WEKA完成的，该软件负责为数据挖掘应用程序（即分类）实施各种机器学习算法。这项工作提供了所选机器学习分类模型的基本概述，并简要介绍了三个战略棋盘游戏的数据集。然后，它使用K折交叉验证测试模式评估和比较各种分类器的预测性能。结果基于几个评估指标，例如准确性，精度，召回率，kappa统计信息，平均绝对误差和均方根误差。最后，它为战略棋盘游戏的结果预测提供了最佳分类方法。基于树的LMT，基于SVM的SMO和基于K-NN的LBk被认为是最适合战略棋盘游戏结果预测的模型，而LMT是最有影响力的模型。

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