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In this research paper, we aim to investigate and address the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. We identify two main challenges associated with these methods. Firstly, the feature ranking criterion utilized in these approaches is inconsistent with the maximum-margin theory. Secondly, the computation of the criterion is performed locally, lacking the ability to measure the importance of features globally. To overcome these challenges, we propose a novel feature ranking criterion called Maximum Margin and Global (MMG) criterion. This criterion utilizes the classification margin to determine the importance of features and computes it globally, enabling a more accurate assessment of feature importance. Moreover, we introduce an optimal feature subset evaluation algorithm that leverages the MMG criterion to determine the best subset of features. To enhance the efficiency of the proposed algorithms, we provide two alpha seeding strategies that significantly reduce computational costs while maintaining high accuracy. These strategies offer a practical means to expedite the feature selection process. Through extensive experiments conducted on ten benchmark datasets, we demonstrate that our proposed algorithms outperform current state-of-the-art methods. Additionally, the alpha seeding strategies yield significant speedups, further enhancing the efficiency of the feature selection process.
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Perfilación de la Expresión Génica , Máquina de Vectores de Soporte , Perfilación de la Expresión Génica/métodos , AlgoritmosRESUMEN
In feature selection research, simultaneous multi-class feature selection technologies are popular because they simultaneously select informative features for all classes. Recursive feature elimination (RFE) methods are state-of-the-art binary feature selection algorithms. However, extending existing RFE algorithms to multi-class tasks may increase the computational cost and lead to performance degradation. With this motivation, we introduce a unified multi-class feature selection (UFS) framework for randomization-based neural networks to address these challenges. First, we propose a new multi-class feature ranking criterion using the output weights of neural networks. The heuristic underlying this criterion is that "the importance of a feature should be related to the magnitude of the output weights of a neural network". Subsequently, the UFS framework utilizes the original features to construct a training model based on a randomization-based neural network, ranks these features by the criterion of the norm of the output weights, and recursively removes a feature with the lowest ranking score. Extensive experiments on 15 real-world datasets suggest that our proposed framework outperforms state-of-the-art algorithms. The code of UFS is available at https://github.com/SVMrelated/UFS.git.
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Algoritmos , Análisis por Micromatrices , Redes Neurales de la Computación , Distribución AleatoriaRESUMEN
Model selection is an important issue in support vector machine-based recursive feature elimination (SVM-RFE). However, performing model selection on a linear SVM-RFE is difficult because the generalization error of SVM-RFE is hard to estimate. This paper proposes an approximation method to evaluate the generalization error of a linear SVM-RFE, and designs a new criterion to tune the penalty parameter C. As the computational cost of the proposed algorithm is expensive, several alpha seeding approaches are proposed to reduce the computational complexity. We show that the performance of the proposed algorithm exceeds that of the compared algorithms on bioinformatics datasets, and empirically demonstrate the computational time saving achieved by alpha seeding approaches.
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Algoritmos , Máquina de Vectores de Soporte , Biología Computacional/métodos , Análisis DiscriminanteRESUMEN
This paper presents a recursive feature elimination (RFE) mechanism to select the most informative genes with a least square kernel extreme learning machine (LSKELM) classifier. Describing the generalization ability of LSKELM in a way that is related to small norm of weights, we propose a ranking criterion to evaluate the importance of genes by the norm of weights obtained by LSKELM. The proposed method is called LSKELM-RFE which first employs the original genes to build a LSKELM classifier, and then ranks the genes according to their importance given by the norm of output weights of LSKELM and finally removes a "least important" gene. Benefiting from the random mapping mechanism of the extreme learning machine (ELM) kernel, there are no parameter of LSKELM-RFE needs to be manually tuned. A comparative study among our proposed algorithm and other two famous RFE algorithms has shown that LSKELM-RFE outperforms other RFE algorithms in both the computational cost and generalization ability.
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Algoritmos , Perfilación de la Expresión Génica , Perfilación de la Expresión Génica/métodos , Técnicas Genéticas , Análisis de los Mínimos Cuadrados , Máquina de Vectores de SoporteRESUMEN
Embedded feature selection algorithms, such as support vector machine based recursive feature elimination (SVM-RFE), have proven to be effective for many real applications. However, due to the model selection problem, SVM-RFE naturally suffers from a heavy computational burden as well as high computational complexity. To solve these issues, this paper proposes using an optimized extreme learning machine (OELM) model instead of SVM. This model, referred to as OELM-RFE provides an efficient active set solver for training the OELM algorithm. We also present an effective alpha seeding algorithm to efficiently solve successive quadratic programming (QP) problems inherent in OELM. One of the salient characteristics of OELM-RFE is that it has only one tuning parameter: the penalty constant C. Experimental results from work on benchmark datasets show that OELM-RFE tends to have higher prediction accuracy than SVM-RFE, and requires fewer model selection efforts. In addition, the alpha seeding method works better on more datasets.
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Algoritmos , Máquina de Vectores de SoporteRESUMEN
Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.
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Water-soluble porphyrins can be phase transferred by the hyperbranched multiarm copolymer PEI-PZLys; good catalytic activities and recyclabilities were observed for oxidation catalyzed by the encapsulated porphyrins.
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Hemo/química , Péptidos/química , Polímeros/química , Porfirinas/química , Biomimética , Catálisis , Estructura Molecular , Transición de Fase , Solubilidad , Espectrometría de Fluorescencia , Agua/químicaRESUMEN
To evaluate the expression of cyclin dependent kinase inhibitor P27(Kip1) in leukemia and to investigate its clinical significance, the P27(Kip1) protein in bone marrow or peripheral blood samples from 82 cases of leukemia was measured by Western blot and enhanced chemoluminescence (ECL). The results showed that the expression of P27(Kip1) protein in ALL was higher than that in ANLL (P = 0.033) and also that in CML (P = 0.008). P27(Kip1) expression in CLL was higher than that in CML too (P = 0.017). In acute leukemia, the effective rate (CR and PR) of initial chemical therapy in the group of P27(Kip1) > 0.655 was higher than that in the group of P27(Kip1) < or = 0.655, P = 0.041. For ANLL and ALL patients, the survival time in the group of P27(Kip1) > 0.655 was longer than that in the group of P27(Kip1) < or = 0.655, P = 0.0065. There were similar statistical significance for ANLL and ALL patients, P = 0.0271 and P = 0.0266 respectively. There was a negative correlation between chromosomal abnormalities and P27(Kip1) expression in ALL patients (r = -0.775, P = 0.04). The expression of P27(Kip1) protein appeared nothing to do with sex, age, white blood cell number, blast cell number in peripheral blood, serum LDH or uric acid. In conclusion, the expression level of P27(Kip1) protein is in relation to the effect of initial chemical therapy and survival time, so that the lower P27(Kip1) expression may associated with poor prognosis in acute leukemia.