Imbalanced data is a type of data where there exists a difference in the ratio of classes. It occurs easily in real life of data analysis. In Data mining the functioning of learning algorithms caused by the imbalanced data. Most of the machine learning algorithms has a tendency to prejudice towards the class of majority in case of imbalanced data and hence those algorithms misjudge the minority class. Therefore, In this article we discuss a systematic way to address the imbalanced data classification problem by applying the rule based ensemble learning techniques like bagging, boosting, voting and stacking to build models, and then accelerates the performance of learning algorithms. In this research, we have preferred real data of chronic kidney disease which is collected from Appolo Hospitals, Tamil Nadu, India, to predict kidney disease of patients .The collected data is initially imbalanced. Firstly, the imbalanced data is balanced by applying SMOTE algorithm, which is an over sampling technique. Then applied various ensemble learning techniques to make better prediction. The incurred results showed that the model template chosen can minimize the problem of misclassification of imbalanced data efficaciously. But this model template cannot classify correctly when imbalanced rate of class increases i.e. in case of Big Data. For better result of imbalanced Big Data, new algorithmic plan of action has to be exploited which can be measured by using Hadoop framework and mapreduce programming model.

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Feature Selection (FS) is an imperative issue in data mining and machine learning. It is an inevitable task to shorter the number of features presented in the initial data set for better classification result, minimized computation time, and reduced memory consumption. In this article, a novel framework using Correlation Coefficient (CCE) and Symmetrical Uncertainty (SU) for selecting the subset of feature is proposed. The selected features are congregated into finite number of clusters by grading their CCE and comparing the SU values. In each cluster, a feature with maximum SU value is retained while remaining features in the same cluster are ignored. The proposed framework was examined with Ten(10) real time benchmark data sets. Experimental outcomes show that the proposed method is outruns than majority of conventional feature selection methods(Information Gain, Chi-Square, Gain Ratio, ReliefF) in accuracy. This method is tested using Tree Based, Rule Based, Lazy, and Naive Bayes learners.

The objective of current study is to increase the classification accuracy of learning algorithms over cardiotocography data by applying preprocessing technique. Due to the diversity of sources, large amount of data is being generated and also has various problems including mislabeled data, missing values, noise, high dimensionality and imbalanced class labels. Method: In this study, we suggested a technique to handle imbalanced data to increase the classification performance of various lazy learners, rule based induction models and tree based models. We used Symmetric Minority Over Sampling Technique (SMOTE) on real dataset to accelerate the performance of various classifiers. We identified that primary dataset is suffering with imbalanced problem, which means the most of the records belong to same class label. Prediction of imbalanced data is biased towards the class with majority instances. To overcome this problem, dataset has to be balanced. Results: As a result of the suggested method the performance of classification algorithms are increased. The obtained result show that majority of classification techniques performed better over balanced dataset when compared with imbalanced dataset. Conclusion: Classification performance over balanced dataset has recorded improved performance than imbalanced dataset after applying the SMOTE.

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In recent days, due to the advancements in technology, a massive amount of data is generating in every area of study, including the medical field. This massive amount of data contains a large number of attributes and instances in it. It is not an easy task for classification and prediction from this high dimensional data. Because, all the attributes in the dataset can't give an impressive result in classification and prediction. Now, it is unavoidable to reduce the high dimensional data for better classification result, which is possible by feature selection and reduction techniques .In this research paper, a novel M-Cluster feature selection (Mcfs) based on Symmetrical Uncertainty (SU) Attribute Evaluator is proposed for improving the classification accuracy of medical datasets. The proposed approach divides the total feature space into 'M' clusters, each cluster has a finite set of attributes in it without any duplication. Feature subset formed by proposed technique is tested using Dermatology and Breast Cancer medical datasets, and compared with an existing filter-based feature selection techniques(Information Gain (IG), Chi-Squared (Chi), Gain Ratio Attribute Evaluator (GR), ReliefF (Rel) ).Experimental results displayed an improved performance with some of the clusters formed by proposed method than existing methods. For experimenting proposed technique, KNN-Lazy learner, Naive Bayes (NB) Classifier, J48-Rule based learner, JRip -Tree based learners are used.