various Issues regarding Classification and Prediction in data mining

various Issues regarding Classification and Prediction in data mining

There are the following pre-processing steps that can be used to the data to facilitate boost the accuracy, effectiveness, and scalability of the classification or prediction phase which are as follows −

Data cleaning-This defines the pre-processing of data to eliminate or reduce noise by using smoothing methods and the operation of missing values (e.g., by restoring a missing value with the most generally appearing value for that attribute, or with the best probable value established on statistics). Although various classification algorithms have some structures for managing noisy or missing information, this step can support reducing confusion during learning.

Relevance analysis-There are various attributes in the data that can be irrelevant to the classification or prediction task. For instance, data recording the day of the week on which a bank loan software was filled is improbable to be relevant to the success of the software. Moreover, some different attributes can be redundant.

Therefore, relevance analysis can be implemented on the data to delete some irrelevant or redundant attributes from the learning procedure. In machine learning, this step is referred to as feature selection. It contains such attributes that can otherwise slow down, and likely mislead the learning step.

Correctly, the time used on relevance analysis, when inserted to the time used on learning from the resulting “reduced” feature subset, and must be less than the time that would have been used on learning from the initial set of features. Therefore, such analysis can help boost classification effectiveness and scalability.

Data transformation-The data can be generalized to a larger-level approach. Concept hierarchies can be used for these goals. This is especially helpful for continuous-valued attributes. For instance, mathematical values for the attribute income can be generalized to the discrete field including low, medium, and high. Likewise, nominal-valued attributes, such as the street, can be generalized to larger-level concepts, such as the city.

Rule-based Classification in Data Mining

Rule-based classification in data mining is a technique in which class decisions are taken based on various “if...then… else” rules. Thus, we define it as a classification type governed by a set of IF-THEN rules. We write an IF-THEN rule as:

“IF condition THEN conclusion.”

IF-THEN Rule

To define the IF-THEN rule, we can split it into two parts:

•Rule Antecedent: This is the “if condition” part of the rule. This part is present in the LHS(Left Hand Side). The antecedent can have one or more attributes as conditions, with logic AND operator.

•Rule Consequent: This is present in the rule's RHS(Right Hand Side). The rule consequent consists of the class prediction.