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Cancer classification based on molecular level investigation has gained the interest of researches as it provides a systematic, accurate and objective diagnosis for different cancer types. Several recent researches have been studying the problem of cancer classification using data mining methods, machine learning algorithms and statistical methods to reach an efficient analysis for gene expression profiles. Studying the characteristics of thousands of genes simultaneously offered a deep insight into cancer classification problem. It introduced an abundant amount of data ready to be explored. It has also been applied in a wide range of applications such as drug discovery, cancer prediction and diagnosis which is a very important issue for cancer treatment. Besides, it helps in understanding the function of genes and the interaction between genes in normal and abnormal conditions. That is done by monitoring the behavior of genes -gene expression data- under different conditions. In this paper, an effective ensemble approach is proposed. Ensemble classifiers increase not only the performance of the classification, but also the confidence of the results. The motivations beyond using ensemble classifiers are that the results are less dependent on peculiarities of a single training set and because the ensemble system outperforms the performance of the best base classifier in the ensemble. [ABSTRACT FROM AUTHOR]

Data mining and Machine learning in Sports analytics is a recent field in Computer Science. In this paper our goal is to predict the outcome of an ODI (One Day International) Cricket match. Outcome of an ODI Cricket match depends on several factors such as home game advantage, Day/Night, Toss, Innings (first or second), physical fitness of teams and dynamic strategies, a lot of which varies as the game proceeds. We have applied modern classification techniques –Naïve Bayesian, Support Vector Machines, and Random Forest, and conducted a comparative study based on their outcomes and performances. Based on the outcome of these models we have developed a tool COP (Cricket Outcome Predictor), which outputs the win/loss probability of an ODI match. The target audience of this tool involves teams playing cricket, and Sports Analysts in general. [ABSTRACT FROM AUTHOR]