Hello. The combination of different predictive models is often called an ensemble. Indeed, it is empirically shown to improve the predictive performance since it either combines a lot of simple models with high bias or more complex models with high variance.

These are the most common flavors of ensembling:

* Averaging/majority voting: train a bunch of classifiers/regression models, take the predictions of each of the models and combine them. In the case of regression, this can be a (weighted) average. In the case of classification, a (weighted) majority voting scheme can be applied.

* Boosting: iteratively train simplistic models with high bias, and in the next iteration focus on the samples that are wrongly classified by the model in the previous iteration.

* Stacking/Blending: feed the predictions (vector of probabilities in case of classification) to another model (second-level) model. This works really well, as many winning solutions on Kaggle will show.

thank you for your valuable information

Ensemble Learning is the key to your answer. It has been there for a very long time. Here are some very good papers that you might want to look for it:

1. http://www.sciencedirect.com/science/article/pii/S002200009791504X

2. http://ieeexplore.ieee.org/document/5299216/

And for summarizing things up and get a quick hands-on for Ensemble Learning check this out: http://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/