Input Variable Selection Using Independent Component Analysis and Higher Order Statistics
نویسندگان
چکیده
In real world problems of nonlinear model building there may be a number of inputs available for use. However, a common problem is that we do not know which inputs are necessary for the model. Previous methods have difficulties in coping with dependent inputs. In this paper, we propose a novel method of input variable selection based on independent component analysis and higher order cross statistics. Experimental results indicate that the method is capable of giving reliable performance with dependent inputs to nonlinear models.
منابع مشابه
Selecting inputs for modeling using normalized higher order statistics and independent component analysis
The problem of input variable selection is well known in the task of modeling real-world data. In this paper, we propose a novel model-free algorithm for input variable selection using independent component analysis and higher order cross statistics. Experimental results are given which indicate that the method is capable of giving reliable performance and that it outperforms other approaches w...
متن کاملInput variable selection using independent component analysis
The problem of input variable selection is well known in the task of modeling real world data. In this paper, we propose a novel model-free algorithm for input variable selection using independent component analysis and higher order cross statistics. Experimental results are given which indicate that the method is capable of giving reliable performance and that it outperforms other approaches w...
متن کاملEfficiency Measurement of Clinical Units Using Integrated Independent Component Analysis-DEA Model under Fuzzy Conditions
Background and Objectives: Evaluating the performance of clinical units is critical for effective management of health settings. Certain assessment of clinical variables for performance analysis is not always possible, calling for use of uncertainty theory. This study aimed to develop and evaluate an integrated independent component analysis-fuzzy-data envelopment analysis approach to accurate ...
متن کاملSelection of multinomial logit models via association rules analysis
In this research, we propose a novel approach for a multinomial logit model selection procedure: specifically, we apply association rules analysis to identifying potential interactions for multinomial logit modeling. Interaction effects are very common in reality, but conventional multinomial logit model selection methods typically ignore them. This is especially true for higher-order interacti...
متن کاملBootstrap feature selection in support vector machines for ventricular fibrillation detection
Support Vector Machines (SVM) for classification are being paid special attention in a number of practical applications. When using nonlinear Mercer kernels, the mapping of the input space to a highdimensional feature space makes the input feature selection a difficult task to be addressed. In this paper, we propose the use of nonparametric bootstrap resampling technique to provide with a stati...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998