Selective Feature Generation Method for Classification of Low-Dimensional Data

Abstract

We propose a method that generates input features to effectively classify low-dimensional data. To do this, we first generate high-order terms for the input features of the original low-dimensional data to form a candidate set of new input features. Then, the discrimination power of the candidate input features is quantitatively evaluated by calculating the 'discrimination distance' for each candidate feature. As a result, only candidates with a large amount of discriminative information are selected to create a new input feature vector, and the discriminant features that are to be used as input to the classifier are extracted from the new input feature vectors by using a subspace discriminant analysis. Experiments on low-dimensional data sets in the UCI machine learning repository and several kinds of low-resolution facial image data show that the proposed method improves the classification performance of low-dimensional data by generating features.

Visual Aggregation of Spatial Recognition Scientific Computations in the Financial Service Sector Based on the Random Forest Graph Model

“…e matrix of state transition in state 1 is converted to state 2 until the end position as the random forest graph model (c k ) is classified in the category of financial service sector [16,17], with A k c state transition matrix:…”