Editorial

Advances in extreme learning machines (ELM2010)

Classification of motor imagery electroencephalogram (EEG) is one of the most important technologies for BCI. To improve the accuracy, this paper introduces a classification system based on Multilayer Extreme Learning Machine (ML-ELM). In the system, the combination of PCA and LDA is chosen as the method of feature extraction and the ML-ELM is used to classify. The ML-ELM has not only the advantage which ELM has but also better performance than ELM. In the experiment, our method is compared with the methods based on ELM, such as kernel-ELM, Constrained-ELM and V-ELM, and some state-of–the-art methods on the same dataset. The experimental results show that ML-ELM is much more suitable for motor imagery EEG data and has better performance than the others.

Machine learning techniques are being increasingly used for detection and diagnosis of diseases for its accuracy and efficiency in pattern classification. In this paper, improved cuckoo search based extreme learning machine (ICSELM) is proposed to classify binary medical datasets. Extreme learning machine (ELM) is widely used as a learning algorithm for training single layer feed forward neural networks (SLFN) in the field of classification. However, to make the model more stable, an evolutionary algorithm improved cuckoo search (ICS) is used to pre-train ELM by selecting the input weights and hidden biases. Like ELM, Moore–Penrose (MP) generalized inverse is used in ICSELM to analytically determines the output weights. To evaluate the effectiveness of the proposed model, four benchmark datasets, i.e. Breast Cancer, Diabetes, Bupa and Hepatitis from the UCI Repository of Machine Learning are used. A number of useful performance evaluation measures including accuracy, sensitivity, specificity, confusion matrix, Gmean, F-score and norm of the output weights as well as the area under the receiver operating characteristic (ROC) curve are computed. The results are analyzed and compared with both ELM based models like ELM, on-line sequential extreme learning algorithm (OSELM), CSELM and other artificial neural networks i.e. multi-layered perceptron (MLP), MLPCS, MLPICS and radial basis function neural network (RBFNN), RBFNNCS, RBFNNICS. The experimental results demonstrate that the ICSELM model outperforms other models.

Extreme learning machine (ELM) has been an important research topic over the last decade due to its high efficiency, easy-implementation, unification of classification and regression, and unification of binary and multi-class learning tasks. Though integrating these advantages, existing ELM algorithms pay little attention to optimizing the choice of kernels, which is indeed crucial to the performance of ELM in applications. More importantly, there is the lack of a general framework for ELM to integrate multiple heterogeneous data sources for classification. In this paper, we propose a general learning framework, termed multiple kernel extreme learning machines (MK-ELM), to address the above two issues. In the proposed MK-ELM, the optimal kernel combination weights and the structural parameters of ELM are jointly optimized. Following recent research on support vector machine (SVM) based MKL algorithms, we first design a sparse MK-ELM algorithm by imposing an ℓ₁-norm constraint on the kernel combination weights, and then extend it to a non-sparse scenario by substituting the ℓ₁-norm constraint with an ℓ_p-norm $(p>1)$ constraint. After that, a radius-incorporated MK-ELM algorithm which incorporates the radius of the minimum enclosing ball (MEB) is introduced. Three efficient optimization algorithms are proposed to solve the corresponding kernel learning problems. Comprehensive experiments have been conducted on Protein, Oxford Flower17, Caltech101 and Alzheimer׳s disease data sets to evaluate the performance of the proposed algorithms in terms of classification accuracy and computational efficiency. As the experimental results indicate, our proposed algorithms can achieve comparable or even better classification performance than state-of-the-art MKL algorithms, while incurring much less computational cost.

This paper proposes an evolutionary wrapper feature selection using Extreme Learning Machines (ELM) as the base classifier training algorithm, comprising a Genetic Algorithm (GA) exploring the space of feature combinations. GA fitness function is the mean accuracy of a cross-validation evaluation of each individual feature selection. The marginal distribution of the classification accuracy corresponding to a feature is used to measure feature saliency. The raw features are extracted as a voxel selection from anatomical brain magnetic resonance imaging (MRI). Voxel selection is provided by Voxel Based Morphometry (VBM) which finds statistically significant clusters of voxels that have differences across MRI volumes on a paired dataset of Alzheimer's Disease (AD) and healthy controls.

This paper focuses on the application of Extreme Learning Machines (ELM) to the classification of remote sensing hyperspectral data. The specific aim of the work is to obtain accurate thematic maps of soybean crops, which have proven to be difficult to identify by automated procedures. The classification process carried out is as follows: First, spectral data is transformed into a hyper-spherical representation. Second, a robust image gradient is computed over the hyper-spherical representation allowing an image segmentation that identifies major crop plots. Third, feature selection is achieved by a greedy wrapper approach. Finally, a classifier is trained and tested on the selected image pixel features. The classifiers used for feature selection and final classification are Single Layer Feedforward Networks (SLFN) trained with either the ELM or the incremental OP-ELM. Original image pixel features are computed following a Functional Data Analysis (FDA) characterization of the spectral data. Conventional ELM training of the SLFN improves over the classification performance of state of the art algorithms reported in the literature dealing with the data treated in this paper. Moreover, SLFN-ELM uses less features than the referred algorithms. OP-ELM is able to find competitive results using the FDA features from a single spectral band.

In this paper a novel optimization algorithm, which utilizes the key ideas of both genetic algorithm (GA) and extreme learning machine (ELM), is proposed. Traditional genetic algorithm employs genetic operations, such as selection, mutation and crossover to generate the optimal solution. In practice, the child solutions generated by crossover and mutation are largely random and therefore cannot ensure the fast convergence of the algorithm. To tackle the weakness of traditional GA, the ELM is introduced to estimate the nonlinear functional relationships between the parent population and child population generated by genetic operations. The trained downward-climbing and upward-climbing ELMs are then employed to generate candidate solutions, which forms the new population together with the solutions given by genetic operations. The proposed algorithm is applied to the power system economic dispatch problem. As demonstrated in case studies, the modified genetic algorithm is able to locate local minima faster and escape from local minima with a greater probability. The proposed algorithm can therefore ensure the faster convergence and provide more economical dispatch plans.