A Genetic Programming Based ECOC Algorithm for Microarray Data Classification

Error-Correcting Output Codes (ECOC) provides an effective solution for the multiclass classification problem by decomposing a multiclass problem into a set of binary class problems. In an ECOC algorithm, the design of coding matrix is the key to its performance. In this paper, we propose a Genetic Programming (GP) based ECOC algorithm, aiming to produce optimal coding matrices through the evolutionary process. In our GP, each terminal node denotes a column in the coding matrix, and each nonterminal node represents an operator, which combines the columns represented by its terminal nodes. In this way, an individual is interpreted as a coding matrix, and a set of operators are proposed to exchange information between column pairs, so as to produce new columns. Feature selection methods are also integrated into the terminal nodes, so that individuals are dynamically assigned to optimal feature subspaces for diverse classification problems. With evolutionary operators, offspring with high discriminant capability would be produced in the evolution. Our experiments compare our algorithm with other 7 classic ECOC algorithms with the deployment of diverse basic classifiers based on a set of UCI data sets, and results prove the superiority and robustness of our algorithm.

Abstract. Image processing has been one of the efficient technologies for GIS data requisition. Support Vector Machines (SVMs) have peculiar advantages in handling problems with small sample sizes, nonlinearity, and high dimensionality. However, SVMs can only solve two-class problems while multi-class decision is impossible. Error correcting output coding (ECOC) SVMs enhance the ability of fault tolerance when solving multi-class classification problems, which makes ECOC SVMs suitable for remote sensing image classification. In this paper, the generalization ability of ECOC SVMs is discussed. ECOC SVMs with optimum coding matrices are selected by experiment, and applied to remote sensing image classification. Experimental results show that, compared with Conventional multi-class classification methods, less SVM sub-classifiers are needed for ECOC SVMs in remote sensing image classification, and the classification accuracy is also improved.

Recursive ECOC classification

The Error-Correcting Output Codes (ECOC) method reduces the multi-class learning problem into a series of binary classifiers. In this paper, we propose a modified Hadamard-type ECOC method. This method uses both N’th order and N/2’th-order Hadamard matrix to construct error correcting output codes, which is called Hybrid Hadamard ECOC. Experiments based on dichotomizers of Support Vector Machines (SVM) have been carried out to evaluate the performance of the proposed method. When compared to normal Hadamard ECOC, computation of the method is reduced greatly while the accuracy of classification only drops slightly.

The diverse microarray datasets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions. To aid in the analysis of such large compendia of microarray experiments, we present Microarray Experiment Functional Integration Technology (MEFIT), a scalable Bayesian framework for predicting functional relationships from integrated microarray datasets. Furthermore, MEFIT predicts these functional relationships within the context of specific biological processes. All results are provided in the context of one or more specific biological functions, which can be provided by a biologist or drawn automatically from catalogs such as the Gene Ontology (GO). Using MEFIT, we integrated 40 Saccharomyces cerevisiae microarray datasets spanning 712 unique conditions. In tests based on 110 biological functions drawn from the GO biological process ontology, MEFIT provided a 5% or greater performance increase for 54 functions, with a 5% or more decrease in performance in only two functions.

We propose the design of output codes for solving the classification problem in Fast Covering Learning Algorithm (FCLA). For a complex multi-class problem normally the classifiers are constructed by combining the outputs of several binary ones. In this paper, we use the basic methods of decomposition; one per class (OPC) and Error Correcting Output Code (ECOC) with FCLA, binary to binary mapping algorithm as a base binary learner. The methods have been tested on Fisher’s well-known Iris data set and experimental results show that the classification ability is improved by using ECOC method.

Decoding design based on posterior probabilities in Ternary Error-Correcting Output Codes

Error-correcting output codes based ensemble feature extraction

A new approach based on the implementation of support vector machine (SVM) with the error correcting output codes (ECOC) is presented for recognition of multi-class protein folds. The experimental show that the proposed method can improve prediction accuracy by 4%-10% on two datasets containing 27 SCOP folds.

Comparison between error correcting output codes and fuzzy support vector machines

When using convolutional neural networks in different applications, human errors may occur in labeling the data samples. To solve this problem, a self error detection and correction based on Error Correcting Output Code (SEDC-ECOC) method is proposed in this paper. The SEDC-ECOC method works in two stages. In the first stage, the distance between each sample and each class is measured using ECOC, which provides the base of error detection and correction. In the second stage, SVM ECOC conducts further correction as well as plays the role of classification layer in deep networks. Having the advantages of simple construction and independence from deep networks, the SEDC-ECOC method can be applied with different convolutional neural networks. The experimental results show that the proposed method achieves high correction performance for MNIST and CIFAR-10 datasets. Up to 56.09% and 92.11% erroneous sample labels are corrected by applying the proposed method once and twice respectively to noisy labels.

Doppler ultrasound signals analysis using multiclass support vector machines with error correcting output codes

In recent years, blended learning models combining online and offline instructional methods have been widely implemented in higher education institutions. Online learning platforms accumulate a large number of fine-grained records of students' learning behavior, offering an excellent opportunity to analyze students' learning behavior. Firstly, a year's worth of student behavior log data was collected from the “Java Language Programming” blended learning course at a college. Secondly, we propose an interpretable grade prediction method based on the error correcting output code (ECOC) with Shapley theory. This approach employs Recursive Feature Elimination (RFE) to select features from the original features, and creates an ECOC classification matrix to predict student performance. Finally, the Shapley values for each feature were used to conduct an explainable analysis of the prediction results. Experimental results show that the proposed method outperforms other classical classification algorithms. This study provides a visual and educational analysis of the major factors affecting academic performance, thereby offering educational administrators more intuitive decision support.

Lung cancer has been a leading cause of cancer-related mortality in recent years, and early detection can increase patients’ chances of recovery. Machine learning and image processing may be used to analyze Computed Tomography (CT) scans for signs of lung cancer; by integrating several machine learning models, the accuracy of lung cancer diagnoses can be increased. In this paper, we propose a method that introduces a segmentation algorithm based on Social Spider Optimization (SSO) to detect suspicious regions in the CT image. The proposed method uses a combination of Error Correcting Output Codes (ECOC) and Support Vector Machine (SVM) to classify suspicious regions and diagnose lung cancer. The efficiency has been evaluated and compared with previous works. The results show that the proposed method can diagnose lung cancer with an average accuracy of 96.67% and can be used as an efficient tool for assisting specialists in diagnosing lung cancer.

This paper presents the use of multiclass support vector machines (SVMs) for diagnosis of spirometric patterns (normal, restrictive, obstructive). The SVM decisions were fused using the error correcting output codes (ECOC). The multiclass SVM with the ECOC was trained on three spirometric parameters (forced expiratory volume in 1s--FEV1, forced vital capacity--FVC and FEV1/FVC ratio). The total classification accuracy of the SVM is 97.32%. The obtained results confirmed the validity of the SVMs to help in clinical decision-making.