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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.