Data Mining Practical Machine Learning Tools and Techniques

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

/pdf/mrmr-abc-a-hybrid-gene-selection-algorithm-for-cancer-5aazknkqqi.pdf

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

The CBPLR showed superior results in terms of AUR and misclassification rate.In terms of the number of selected genes, the CBPLR outperformed APLR and LASSO.The CBPLR performed remarkably well in stability test.The classification accuracy for the CBPLR method is quite consistent and high. An important application of DNA microarray data is cancer classification. Because of the high-dimensionality problem of microarray data, gene selection approaches are often employed to support the expert systems in diagnostic capability of cancer with high classification accuracy. Penalized logistic regression using the least absolute shrinkage and selection operator (LASSO) is one of the key steps in high-dimensional cancer classification, as gene coefficient estimation and gene selection simultaneously. However, the LASSO has been criticized for being biased in gene selection. The adaptive LASSO (APLR) was originally proposed to overcome the selection bias by assigning a consistent weight to each gene. In high-dimensional data, however, the adaptive LASSO faces practical problems in choosing the type of initial weight. In practice, the LASSO estimator itself has been used as an initial weight. However, this may not be preferable because the LASSO is inconsistent in itself. To address this issue, an alternative initial weight in adaptive penalized logistic regression (CBPLR) is proposed. The effectiveness of the CBPLR is examined on three well-known high-dimensional cancer classification datasets using number of selected genes, area under the curve, and misclassification rate. The experimental results reveal that the proposed CBPLR is quite efficient and feasible for cancer classification. Additionally, the proposed weight is compared with APLR and LASSO and exhibits competitive performance in both classification accuracy and gene selection. The proposed CBPLR has significant impact in penalized logistic regression by selecting fewer genes with high area under the curve and low misclassification rate. Thus, the proposed weight could conceivably be used in other research that implements gene selection in the field of high dimensional cancer classification.

https://isiarticles.com/bundles/Article/pre/pdf/46593.pdf

Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.

C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods.

Microarray data classification is a task involving high dimensionality and small samples sizes. A common criterion to decide on the number of selected genes is maximizing the accuracy, which risks overfitting and usually selects more genes than actually needed. We propose, relaxing the maximum accuracy criterion, to select the combination of attribute selection and classification algorithm that using less attributes has an accuracy not statistically significantly worst that the best. Also we give some advice to choose a suitable combination of attribute selection and classifying algorithms for a good accuracy when using a low number of gene expressions. We used some well known attribute selection methods (FCBF, ReliefF and SVM-RFE, plus a Random selection, used as a base line technique) and classifying techniques (Naive Bayes, 3 Nearest Neighbor and SVM with linear kernel) applied to 30 data sets involving different cancer types.

Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods

This paper deals with the problem of training an Artificial Neural Network (ANN) when the data sets are very imbalanced. Most learning algorithms, including ANN, are designed for well-balanced data and do not work properly on imbalanced ones. Of the approaches proposed for dealing with this problem, we are interested in the re-sampling ones, since they are algorithm-independent. We have recently proposed a new under-sampling technique for the two-class problem, called Non-Target Incremental Learning (NTIL), which has shown a good performance with SVM, improving results and training speed. Here, the advantages of using this technique with ANN are shown. The performance with regard to other popular under-sampling techniques is compared.

Improving ANN performance for imbalanced data sets by means of the NTIL technique

Client threshold prediction in biometric signature recognition by means of Multiple Linear Regression and its use for score normalization

SIAL is an Intelligent Tutoring System (ITS) for learning Computational Logic. It teaches classical refutation by resolution concepts using Robinson’s Binary Resolution Rule. Furthermore, SIAL can be considered a Model-Based System, as its Domain Model is an Automated Theorem Prover (ATP) for First Order Logic (FOL). This allows SIAL to accept several solutions to the proposed exercise, all of which are correct, providing a kind of open-ended feature to the ITS. The Domain Model is in charge of carrying out the error diagnosis, identifying, in many cases, the misunderstandings. The focus of this paper is to describe the Pedagogical Model of SIAL that takes advantage of the error diagnosis capabilities of the Domain Model to offer a learner-adaptive tutorial action, according to the user cognitive profile.

Learner-Adaptive Pedagogical Model in SIAL, an Open-Ended Intelligent Tutoring System for First Order Logic

This paper presents a novel approach to estimate (predict) the a priori client decision threshold for biometric recognition systems based on multiple linear regression. Biometric recognition is a complex classification problem where the goal is to classify a pattern (biometric sample) as belonging or not to a certain class (client). As in other pattern recognition problems, a correct estimation of the decision threshold is essential for optimizing the biometric system's performance. Our proposal is tested in biometric signature recognition, estimating thresholds for different system working points. A theoretical and practical performance analysis is presented, including a comparison with the state of the art, showing the advantages, in system performance, of our proposal.

Arancha Simon-Hurtado

Papers

Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods

Improving ANN performance for imbalanced data sets by means of the NTIL technique

Client threshold prediction in biometric signature recognition by means of Multiple Linear Regression and its use for score normalization

Learner-Adaptive Pedagogical Model in SIAL, an Open-Ended Intelligent Tutoring System for First Order Logic

A new approach for a priori client threshold estimation in biometric signature recognition based on multiple linear regression