I'm using a binary classification with SVM and MLP for financial data. My input data has 21 features so I used dimensionally reduction methods for reducing the dimension of data. Some dimensionally reduction methods like stepwise regression report best features so I will used these features for my classification mode and another methods like PCA transform data to a new space and I use for instance 60% of best reported columns (features). The critical problem is in the phase of using final model. For example I used the financial data of past year and two years ago for today financial position. So now I want use past and today data to prediction next year. My question is here: Should I use PCA for new input data before inserting to my designed classification model? How can I use (For example Principal component analysis) for this data? I must use it like before? (pca(newdata…)) or there is some results from last PCA that I must use in this phase?
more information :
This is my system structure: I have a hybrid classification method with optimization algorithm for select best features (inputs) of my model and best parameters of my classification method so for a classification method like MLP I takes long time to optimization with 21 features (beside of this I repeat every iteration of my optimization algorithm 12 times / cross section ) . So I want decrease the features with dimensionally reduction techniques (like PCA, NLPCA or supervised methods like LDA/FDA) before insert it to classification method. For example I’m using this structure of PCA code:
[coeff,score,latent,tsquared,explained,mu] = pca(_)
After that I will use 10 first columns of output (that sorted by PCA function) for input of my classification and optimization model. In final phase I will find the best model parameters with the best combination of inputs. For example my raw data has 21 features. After first phase of using PCA I will choose 10 features and in final model after optimization of my classification model. I will have a model with 5 best chosen features. Now I want use this model with new data. What must I do?
Thank you so much for your kind helps.