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Papers by Indika Wickramasinghe

Discover education, Jun 14, 2024

Journal of sports analytics, Jun 19, 2020

This paper presents findings of a study to predict the winners of an One Day International (ODI) ... more This paper presents findings of a study to predict the winners of an One Day International (ODI) cricket game, after the completion of the first inning of the game. We use Naive Bayes (NB) approach to make this prediction using the data collected with 15 features, comprised of variables related to batting, bowling, team composition, and other. Upon the construction of an initial model, our objective is to improve the accuracy of predicting the winner using some feature selection algorithms, namely univariate, recursive elimination, and principle component analysis (PCA). Furthermore, we examine the contribution of the appropriate ratios of training sample size to testing sample size on the accuracy of prediction. According to the experimental findings, the accuracy of winner-prediction can be improved with the use of feature selection algorithm. Moreover, the accuracy of winner prediction becomes the highest (85.71%) with the univariate feature selection method, compared to its counterparts. By selecting the appropriate ratio of the sample sizes of training sample to testing sample, the prediction accuracy can be further increased.

Soft Computing, Sep 9, 2020

Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but effic... more Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but efficient algorithm with a wide variety of real-world applications, ranging from product recommendations through medical diagnosis to controlling autonomous vehicles. Due to the failure of real data satisfying the assumptions of NB, there are available variations of NB to cater general data. With the unique applications for each variations of NB, they reach different levels of accuracy. This manuscript surveys the latest applications of NB and discusses its variations in different settings. Furthermore, recommendations are made regarding the applicability of NB while exploring the robustness of the algorithm. Finally, an attempt is given to discuss the pros and cons of NB algorithm and some vulnerabilities, with related computing code for implementation.

Machine Learning with Applications

Probability, Statistics, and Stochastic Processes for Engineers and Scientists, 2020

Probability, Statistics, and Stochastic Processes for Engineers and Scientists, 2020

In this study, Polya’s problem-solving method is introduced in a statistics class in an effort to... more In this study, Polya’s problem-solving method is introduced in a statistics class in an effort to enhance students ’ performance. Teaching the method was applied to one of the two introductory-level statistics classes taught by the same instructor, and a comparison was made between the performances in the two classes. The results indicate there was a significant improvement of the students ’ performance in the class in which Polya’s method was introduced.

Log-Linear Models (LLMs) are important techniques used in categorical data analysis. Though there... more Log-Linear Models (LLMs) are important techniques used in categorical data analysis. Though there are some available published work about LLMs, the explanation of model building process and the theoretical background are not adequate. Furthermore, research about the application of the LLM theory and the selection procedure of the best model are handful. Therefore, this manuscript aims to fill that vacuum. At first, the construction of LLM and Hierarchical Log-Linear Models (HLLMs), a branch of LLMs are discussed in connection with both 2 × 2 and 2 × 2 × 2 contingency tables. Secondly, an application is presented to analyze the collected data set about the academic performance of elementary students. The manuscript also discusses the criteria to select the best model that fits the collected data.

Journal of Sports Analytics, 2020

This paper presents findings of a study to predict the winners of an One Day International (ODI) ... more This paper presents findings of a study to predict the winners of an One Day International (ODI) cricket game, after the completion of the first inning of the game. We use Naive Bayes (NB) approach to make this prediction using the data collected with 15 features, comprised of variables related to batting, bowling, team composition, and other. Upon the construction of an initial model, our objective is to improve the accuracy of predicting the winner using some feature selection algorithms, namely univariate, recursive elimination, and principle component analysis (PCA). Furthermore, we examine the contribution of the appropriate ratios of training sample size to testing sample size on the accuracy of prediction. According to the experimental findings, the accuracy of winner-prediction can be improved with the use of feature selection algorithm. Moreover, the accuracy of winner prediction becomes the highest (85.71%) with the univariate feature selection method, compared to its counterparts. By selecting the appropriate ratio of the sample sizes of training sample to testing sample, the prediction accuracy can be further increased.

Journal of Human Sport and Exercise, 2014

Cricket is one of the team games played over 50 countries in different levels. Though the perform... more Cricket is one of the team games played over 50 countries in different levels. Though the performance of each batsman in the team can be easily quantified, the prediction of player performance is arduous. This paper demonstrates a methodology to predict the performance of cricket batsman in testmatch series. In this study, longitudinal test cricket data have been collected over five years of period. A model is developed to predict the player performance as a function of certain characteristics related to the player, the team and the match series. Due to the hierarchical nature of the collected cricket data, a three stage hierarchical linear model is proposed in this investigation. According to the outcome of the analysis, the handedness of the player (batsman) and the rank of the team significantly influence player performance. Finally, an accurate prediction of player performance is conducted using the proposed model.

Operations Research and Decisions, 2016

Factors contributing to winning games are imperative, as the ultimate objective in a game is vict... more Factors contributing to winning games are imperative, as the ultimate objective in a game is victory. The aim of this study is to identify the factors that characterize the game of cricket, and to investigate the factors that truly influence the result of a game using the data collected from the Champions Trophy cricket tournament. According to the results, this cricket tournament can be characterized using the factors of batting, bowling, and decision-making. Further investigation suggests that the rank of the team and the number of runs they score have the most significant influence on the result of games. As far as the effectiveness of assigning bowlers is concerned, the Australian team has done a fabulous job compared to the rest of the teams.

Distributional and inferential results are derived for ARMA models under the multivariate exponen... more Distributional and inferential results are derived for ARMA models under the multivariate exponential power (EP) distribution, which includes the Gaussian and Laplace as special cases. Marginal distributions are obtained in the important Laplace case, both under a multivariate Laplace distributed process, as well as a process driven by univariate Laplace noise. Asymptotic results are established for the maximum likelihood estimators under a full EP likelihood, and under a conditional likelihood resulting from a driving univariate EP noise distribution. Whenever tractability permits, results are fully worked out with respect to the MA(1) model. The methodology is illustrated on a fund of real estate returns.

Probability, Statistics, and Stochastic Processes for Engineers and Scientists, 2020

Discover education, Jun 14, 2024

Journal of sports analytics, Jun 19, 2020

This paper presents findings of a study to predict the winners of an One Day International (ODI) ... more This paper presents findings of a study to predict the winners of an One Day International (ODI) cricket game, after the completion of the first inning of the game. We use Naive Bayes (NB) approach to make this prediction using the data collected with 15 features, comprised of variables related to batting, bowling, team composition, and other. Upon the construction of an initial model, our objective is to improve the accuracy of predicting the winner using some feature selection algorithms, namely univariate, recursive elimination, and principle component analysis (PCA). Furthermore, we examine the contribution of the appropriate ratios of training sample size to testing sample size on the accuracy of prediction. According to the experimental findings, the accuracy of winner-prediction can be improved with the use of feature selection algorithm. Moreover, the accuracy of winner prediction becomes the highest (85.71%) with the univariate feature selection method, compared to its counterparts. By selecting the appropriate ratio of the sample sizes of training sample to testing sample, the prediction accuracy can be further increased.

Soft Computing, Sep 9, 2020

Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but effic... more Naïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but efficient algorithm with a wide variety of real-world applications, ranging from product recommendations through medical diagnosis to controlling autonomous vehicles. Due to the failure of real data satisfying the assumptions of NB, there are available variations of NB to cater general data. With the unique applications for each variations of NB, they reach different levels of accuracy. This manuscript surveys the latest applications of NB and discusses its variations in different settings. Furthermore, recommendations are made regarding the applicability of NB while exploring the robustness of the algorithm. Finally, an attempt is given to discuss the pros and cons of NB algorithm and some vulnerabilities, with related computing code for implementation.

Machine Learning with Applications

Probability, Statistics, and Stochastic Processes for Engineers and Scientists, 2020

Probability, Statistics, and Stochastic Processes for Engineers and Scientists, 2020

In this study, Polya’s problem-solving method is introduced in a statistics class in an effort to... more In this study, Polya’s problem-solving method is introduced in a statistics class in an effort to enhance students ’ performance. Teaching the method was applied to one of the two introductory-level statistics classes taught by the same instructor, and a comparison was made between the performances in the two classes. The results indicate there was a significant improvement of the students ’ performance in the class in which Polya’s method was introduced.

Log-Linear Models (LLMs) are important techniques used in categorical data analysis. Though there... more Log-Linear Models (LLMs) are important techniques used in categorical data analysis. Though there are some available published work about LLMs, the explanation of model building process and the theoretical background are not adequate. Furthermore, research about the application of the LLM theory and the selection procedure of the best model are handful. Therefore, this manuscript aims to fill that vacuum. At first, the construction of LLM and Hierarchical Log-Linear Models (HLLMs), a branch of LLMs are discussed in connection with both 2 × 2 and 2 × 2 × 2 contingency tables. Secondly, an application is presented to analyze the collected data set about the academic performance of elementary students. The manuscript also discusses the criteria to select the best model that fits the collected data.

Journal of Sports Analytics, 2020

This paper presents findings of a study to predict the winners of an One Day International (ODI) ... more This paper presents findings of a study to predict the winners of an One Day International (ODI) cricket game, after the completion of the first inning of the game. We use Naive Bayes (NB) approach to make this prediction using the data collected with 15 features, comprised of variables related to batting, bowling, team composition, and other. Upon the construction of an initial model, our objective is to improve the accuracy of predicting the winner using some feature selection algorithms, namely univariate, recursive elimination, and principle component analysis (PCA). Furthermore, we examine the contribution of the appropriate ratios of training sample size to testing sample size on the accuracy of prediction. According to the experimental findings, the accuracy of winner-prediction can be improved with the use of feature selection algorithm. Moreover, the accuracy of winner prediction becomes the highest (85.71%) with the univariate feature selection method, compared to its counterparts. By selecting the appropriate ratio of the sample sizes of training sample to testing sample, the prediction accuracy can be further increased.

Journal of Human Sport and Exercise, 2014

Cricket is one of the team games played over 50 countries in different levels. Though the perform... more Cricket is one of the team games played over 50 countries in different levels. Though the performance of each batsman in the team can be easily quantified, the prediction of player performance is arduous. This paper demonstrates a methodology to predict the performance of cricket batsman in testmatch series. In this study, longitudinal test cricket data have been collected over five years of period. A model is developed to predict the player performance as a function of certain characteristics related to the player, the team and the match series. Due to the hierarchical nature of the collected cricket data, a three stage hierarchical linear model is proposed in this investigation. According to the outcome of the analysis, the handedness of the player (batsman) and the rank of the team significantly influence player performance. Finally, an accurate prediction of player performance is conducted using the proposed model.

Operations Research and Decisions, 2016

Factors contributing to winning games are imperative, as the ultimate objective in a game is vict... more Factors contributing to winning games are imperative, as the ultimate objective in a game is victory. The aim of this study is to identify the factors that characterize the game of cricket, and to investigate the factors that truly influence the result of a game using the data collected from the Champions Trophy cricket tournament. According to the results, this cricket tournament can be characterized using the factors of batting, bowling, and decision-making. Further investigation suggests that the rank of the team and the number of runs they score have the most significant influence on the result of games. As far as the effectiveness of assigning bowlers is concerned, the Australian team has done a fabulous job compared to the rest of the teams.

Distributional and inferential results are derived for ARMA models under the multivariate exponen... more Distributional and inferential results are derived for ARMA models under the multivariate exponential power (EP) distribution, which includes the Gaussian and Laplace as special cases. Marginal distributions are obtained in the important Laplace case, both under a multivariate Laplace distributed process, as well as a process driven by univariate Laplace noise. Asymptotic results are established for the maximum likelihood estimators under a full EP likelihood, and under a conditional likelihood resulting from a driving univariate EP noise distribution. Whenever tractability permits, results are fully worked out with respect to the MA(1) model. The methodology is illustrated on a fund of real estate returns.

Probability, Statistics, and Stochastic Processes for Engineers and Scientists, 2020