Abstract: Ordinal variables are used to express people's attitudes and preferences towards things.For example,in recommendation system,consumers' grades for goods are ordinal variables,and the emotion in sentiment analysis of NLP is also ordinal variables.At present,the ordered Logit model is adoptedto deal with the ordinal variables.However,the ordered Logit regression mo-del requires that theordinal variables generally follow uniform distribution.When theordinal variables do not conform to uniform distribution,the prediction result of the ordered Logit regression is not ideal.Based on this,this paper proposes an adaptive ensemble ordering algorithm.Firstly,this paper proposes a boosting-like algorithm with the aid of the idea of boosting.According to the concept of the ordered Logit regression model,the ordered multi-layer perceptron model and the ordered random fo-rest model are constructed.The two models,combined with the Softmax multi classification model and the ordered Logit model,constitute a boosting-like algorithm.In data processing,when the prediction values of the four models are not identical,the sample enters the boosting-like model and continues to train until the number of training rounds exceeds a certain threshold.Then,the random fo-rest model is adopted to construct the mapping function from all the predicted values of the training set to the real values.The proposed algorithm has a high prediction accuracy when the ordered variables are arbitrarily distributed,which greatly improves the application scope of the ordered Logit regression model.When the proposed algorithm is applied to the Baijiu quality datasets and the red wine quality datasets,its prediction accuracy is superior to that of the ordered Logit model and Softmax algorithm,Multi-layer Perceptron and KNN.

Key words: Ensemble algorithm, Multi-layer perceptron, Ordered Logit regression model, Ordinal variables, Random forest algorithm