Data Mining Practical Machine Learning Tools and Techniques

International Joint Conference on Neural Networks

Multivariate LSTM-FCNs for time series classification.

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.

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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

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 work presents a novel approach to multivariate time series classification. The method exploits the multivariate structure of the time series and the possibilities of the stacking ensemble method. The basics of the method may be described in three steps: first, decomposing the multivariate time series on its constituent univariate time series; second, inducing a classifier for each univariate time series plus and additional multivariate classifier for the whole time series; third, creating the final multivariate time series classifier stacking the previous classifiers. The ensemble obtained has the potential to improve the accuracy of the single multivariate time series classifier. Several configurations of the stacking method have been tested on seven multivariate time series data sets. In five out of seven data sets, the proposed method obtains the smallest error rate. Moreover, in two out of seven data sets, stacking only the univariate time series classifiers provides the best results. The experimental results show that when a multivariate time series method does not produce an accurate classifier, stacking it with univariate time series classifiers is an alternative worthy of consideration.

Stacking for multivariate time series classification

The systems dynamics and control engineering (FDI) and the artificial intelligence diagnosis (DX) communities have developed complementary approaches that exploit structural relations in the system model to find efficient solutions for the residual generation and residual evaluation steps in fault detection and isolation in dynamic systems. This paper compares three different structural fault diagnosis techniques, two from the DX community and one from the FDI community. To simplify our comparison, we start with bond graphs as the common system modeling language and develop a graph-based framework using temporal causal graphs as the basis for analyzing the three fault isolation approaches. This framework allows for systematic comparison of the diagnosability properties of the three algorithms. The three-tank system is used as a running example to illustrate our concepts and algorithms.

A Common Framework for Compilation Techniques Applied to Diagnosis of Linear Dynamic Systems

The development of efficient and reliable fault detection approaches is necessary to improve performance, safety, and reliability in engineering systems. Moreover, these approaches have to be simple enough to provide quick diagnosis results and to reduce development and maintenance costs. Consistency-based diagnosis using possible conflicts (PCs) relies upon the simulation of numerical models to provide a simple and efficient fault diagnosis approach. However, simulation approaches need to know the initial state, and this assumption is not easily fulfilled in real systems, even in the presence of measurements related to state variables due to noise and parameter uncertainties. In this paper, we develop an approach where PCs are used to automatically compute structural models which can be implemented as simulation and state observer models. Using these models, we propose a framework which integrates those state observers to estimate the initial states for simulation within the consistency-based diagnosis framework. Then, both the simulation models and the state observers are used to provide quick detection decisions without increasing the complexity of the diagnoser. Our integration proposal is open to different kinds of state observers, except for the structural model, and different fault detection configurations. The proposal has been tested on a thermohydraulic reconfigurable laboratory plant using real data with satisfactory results.

Integration of Simulation and State Observers for Online Fault Detection of Nonlinear Continuous Systems

Diagnosis of real world problems demands the integration of different techniques from several research fields. In Model-based Diagnosis, both Artificial Intelligence and Control Theory communities have provided different but complementary approaches. Recent works, known as BRIDGE proposal, provided a common framework for the integration of techniques for static systems.

This work proposes the extension of the BRIDGE framework for a specific class of dynamic systems, thus analyzing the influence of dynamic constraints in the behavior estimation capabilities for two Model-based Diagnosis techniques: Possible Conflicts and Analytical Redundancy Relations obtained through structural analysis. Results show the strong similarities between them, and provide new ways for integration of techniques from both areas. Additionally, algorithms computing Possible Conflicts provide the implicit structural model for state observer design with no extra knowledge added in the model. Results on a case study are provided, then compared and discussed against existing proposals.

Carlos J. Alonso-González

Papers

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

Stacking for multivariate time series classification

A Common Framework for Compilation Techniques Applied to Diagnosis of Linear Dynamic Systems

Integration of Simulation and State Observers for Online Fault Detection of Nonlinear Continuous Systems

Analyzing the influence of differential constraints in possible conflict and ARR computation