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This paper presents a new approach for power system fault classification based on principal component analysis (PCA) and probabilistic neural network (PNN).the work presented in this paper is focused on identification of simple power system faults. The new model mainly includes three steps. Firstly wavelet transform is used to analyze power system fault signals, and distinguishing features are extracted...
Decision trees induction is among powerful and commonly encountered architecture for extracting of classification knowledge from datasets of labeled instances. However, learning decision trees from large irrelevant datasets is quite different from learning small and moderate sized datasets. In this paper, we propose a simple yet effective composite splitting criterion equal to a random sampling approach...
Pattern classification based on transient signal analysis provides an effective method for identification of dynamical systems. The partial-least-square regression (PLSR) is most commonly used to generate parametric representation of phase space defined by measured signals and their time derivatives. The PLS component scores are interpreted as object features for pattern identification. In this paper,...
In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug - like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with...
Sparse data are becoming increasingly common and available in many real-life applications. However, relatively little attention has been paid to effectively model the sparse data and existing approaches such as the conventional “horizontal” and “vertical” representations fail to provide satisfactory performance for both storage and query processing, as such approaches are too rigid and generally do...
Feature extraction methods in pattern recognition tasks seek to transform data variables to abstract mathematical variables such that their scores (called features) reveal hidden data structure of high cognitive value. Various feature extraction methods process raw data from different perspectives. Some depend on statistical correlation or independence such as principal component analysis (PCA), independent...
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