  Predictions combination, as a combination model approach with adjustments in
the output space, has flourished in recent years in research and competitions.
Simple average is intuitive and robust, and is often used as a benchmark in
predictions combination. However, some poorly performing sub-models can reduce
the overall accuracy because the sub-models are not selected in advance. Even
though some studies have selected the top sub-models for the combination after
ranking them by mean square error, the covariance of them causes this approach
to not yield much benefit. In this paper, we suggest to consider the diversity
of sub-models in the predictions combination, which can be adopted to assist in
selecting the most diverse model subset in the model pool using negative
correlation learning. Three publicly available datasets are applied to evaluate
the approach. The experimental results not only show the diversity of
sub-models in the predictions combination incorporating negative correlation
learning, but also produce predictions with accuracy far exceeding that of the
simple average benchmark and some weighted average methods. Furthermore, by
adjusting the penalty strength for negative correlation, the predictions
combination also outperform the best sub-model. The value of this paper lies in
its ease of use and effectiveness, allowing the predictions combination to
embrace both diversity and accuracy.
