J Chem Inf Model - Classifying large chemical data sets: using a regularized potential function method.

Tópicos

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Resumo

In recent years classifiers generated with kernel-based methods, such as support vector machines (SVM), Gaussian processes (GP), regularization networks (RN), and binary kernel discrimination (BKD) have been very popular in chemoinformatics data analysis. Aizerman et al. were the first to introduce the notion of employing kernel-based classifiers in the area of pattern recognition. Their original scheme, which they termed the potential function method (PFM), can basically be viewed as a kernel-based perceptron procedure and arguably subsumes the modern kernel-based algorithms. PFM can be computationally much cheaper than modern kernel-based classifiers; furthermore, PFM is far simpler conceptually and easier to implement than the SVM, GP, and RN algorithms. Unfortunately, unlike, e.g., SVM, GP, and RN, PFM is not endowed with both theoretical guarantees and practical strategies to safeguard it against generating overfitting classifiers. This is, in our opinion, the reason why this simple and elegant method has not been taken up in chemoinformatics. In this paper we empirically address this drawback: while maintaining its simplicity, we demonstrate that PFM combined with a simple regularization scheme may yield binary classifiers that can be, in practice, as efficient as classifiers obtained by employing state-of-the-art kernel-based methods. Using a realistic classification example, the augmented PFM was used to generate binary classifiers. Using a large chemical data set, the generalization ability of PFM classifiers were then compared with the prediction power of Laplacian-modified naive Bayesian (LmNB), Winnow (WN), and SVM classifiers.

Resumo Limpo

recent year classifi generat kernelbas method support vector machin svm gaussian process gp regular network rn binari kernel discrimin bkd popular chemoinformat data analysi aizerman et al first introduc notion employ kernelbas classifi area pattern recognit origin scheme term potenti function method pfm can basic view kernelbas perceptron procedur arguabl subsum modern kernelbas algorithm pfm can comput much cheaper modern kernelbas classifi furthermor pfm far simpler conceptu easier implement svm gp rn algorithm unfortun unlik eg svm gp rn pfm endow theoret guarante practic strategi safeguard generat overfit classifi opinion reason simpl eleg method taken chemoinformat paper empir address drawback maintain simplic demonstr pfm combin simpl regular scheme may yield binari classifi can practic effici classifi obtain employ stateoftheart kernelbas method use realist classif exampl augment pfm use generat binari classifi use larg chemic data set general abil pfm classifi compar predict power laplacianmodifi naiv bayesian lmnb winnow wn svm classifi

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