An Approach for Improving Accuracy of Prediction Using Ensemble Modeling

Abstract

In general terms classification can be divided into two steps. First one is learning step which consists of the predetermined set of classes or concepts. Second step involves testing, in which data sets are being tested for the verification. And after the system is trained with data it can be used for further analysis to be done in future so that future events can be predicted in advance. For different applications we need to apply these models to predict and note the accuracy given by each model. The main aim of the research here is to make such a system which has more accuracy as compared to what previous systems are giving. So to implement this type of system, a hybrid approach is used i.e. ensemble of classifiers. It is necessary that one does not weigh one model purely. Other models or methods can also give more efficient results. In doing so, give weightage to each method and combine these methods to reach final destination that is most informed one. There are a large number of models available which are used for classifying the data into various class labels. That is also known under various other names, such as multiple classifier systems, committee of lassifiers, or mixture of experts. The basic aim of ensemble based systems is shown to produce favourable results compared to those of single-expert systems for a broad range of applications and under a variety of scenarios. There are various procedures available through which the individual classifiers can be combined. These procedures are called as combination rules. Each rule has its different functionality which will work according to requirement and application where it is applied. So the study is carried on the prediction by applying methods and ensemble that with variable seed values. The experiment is carried out on the k-fold validation to check the consistency of the system.