Incomplete Data Decomposition for Classification
نویسنده
چکیده
In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets. Finally, a conflict resolving method is used to combine partial answers from classifiers to obtain final classification. We provide an empirical evaluation of the decomposition method with use of various decomposition criteria.
منابع مشابه
On Decomposition for Incomplete Data
In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets. Finally, a conflict resolving method is used to combine partial answers from classifiers to obtain final classificat...
متن کاملFeature Extraction of Visual Evoked Potentials Using Wavelet Transform and Singular Value Decomposition
Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals. Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data...
متن کاملFast algorithms for Higher-order Singular Value Decomposition from incomplete data
Higher-order singular value decomposition (HOSVD) is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of them involve incomplete data. To obtain HOSVD of the data with missing values, one can first impute the missing entries through a certain tensor completion method and then perform HOSVD...
متن کاملData Decomposition and Decision Rule Joining for Classification of Data with Missing Values
In this paper we present a new approach to handling incomplete information and classifier complexity reduction. We describe a method, called DRJ, that performs data decomposition and decision rule joining to avoid the necessity of reasoning with missing attribute values. In the consequence more complex reasoning process is needed than in the case of known algorithms for induction of decision ru...
متن کاملAn Intelligent Machine Learning-Based Protection of AC Microgrids Using Dynamic Mode Decomposition
An intelligent strategy for the protection of AC microgrids is presented in this paper. This method was halving to an initial signal processing step and a machine learning-based forecasting step. The initial stage investigates currents and voltages with a window-based approach based on the dynamic decomposition method (DDM) and then involves the norms of the signals to the resultant DDM data. T...
متن کامل