Survey on the Comprehensive Review of Different Classification Techniques and Class Imbalance Problem in Data Mining

Data Mining is the process of applying machine learning techniques for automatically or semi automatically analyzing and extracting knowledge from stored data. It is defined as non-trivial extraction of implicit, novel and actionable knowledge from large datasets. Data mining can also be defined as technology which enables data analysis, exploration and visualization of very large databases using high level of abstraction. Data mining models can be categorized as predictive models and descriptive models. Classification is one of the important aspects of data mining which is a predictive modeling technique. Classification is a data mining technique used to predict group membership for data instances .We describes the basic classification techniques. Several major kinds of classification method including decision tree induction,etc, We present a Class imbalance problem has received considerable attention in areas such as Machine Learning and Pattern Recognition.We proposed a class imbalance problem in pattern classification. Data level methods for balancing the classes consists of resampling the original data set, either by over-sampling the minority class or by under-sampling and/or under-sampling the majority class, until the classes are approximately equally represented.