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Feature selection in machine learning: A new perspective
Abstract High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve thisExpand
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Feature Extraction from Tumor Gene Expression Profiles Using DCT and DFT
Feature extraction plays a key role in tumor classification based on gene expression profiles, which can improve the performance of classifier. We design two novel feature extraction methods toExpand
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Gene Selection Using Neighborhood Rough Set from Gene Expression Profiles
Although adopting feature reduction in classic rough set theory to select informative genes is an effective method, its classification accuracy rate is usually not higher compared with otherExpand
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Hybrid Neural Network Based on GA-BP for Personal Credit Scoring
Aiming at the insufficiencies of BP neural network, this paper established a hybrid neural network based on the combination of GA and BP algorithms. The hybrid algorithm made fully use of GA's globalExpand
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Malicious Codes Detection Based on Ensemble Learning
As malicious codes become more complex and sophisticated, the scanning detection method is no longer able to detect various forms of viruses effectively. In this paper, we explore solutions based onExpand
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MOEPGA: A novel method to detect protein complexes in yeast protein-protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm
The identification of protein complexes in protein-protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect proteinExpand
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Neighborhood Rough Set Model Based Gene Selection for Multi-subtype Tumor Classification
Multi-subtype tumor diagnosis based on gene expression profiles is promising in clinical medicine application. Therefore, a great deal of research on tumor classification based on gene expressionExpand
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Gene Selection with Rough Sets for the Molecular Diagnosing of Tumor Based on Support Vector Machines
The development of microarray technology has motivated interest of its use in clinical diagnosis of tumor and drug discovery. However the accurate classification of tumor by selecting theExpand
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Weighted Neighborhood Classifier for the Classification of Imbalanced Tumor Dataset
Machine learning is widely applied to gene expression profiles based molecular tumor classification, but sample imbalance problem is often overlooked. This paper proposed a subclass-weightedExpand
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SVM-Based Tumor Classification with Gene Expression Data
Gene expression data that are gathered from tissue samples are expected to significantly help the development of efficient tumor diagnosis and classification platforms. Since DNA microarrayExpand
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