Samples Of Different Classes Research Articles

A Comparative Study Among Various Statistical Tests Using Microarray Gene Expression Data

This article reviews and compares different existing statistical tests utilized in significance analysis of gene expression from DNA microarray data. Microarray gene expression study facilitates the investigation of genes that are differentially expressed with respect to different classes of samples. Many gene expression studies also aim to find groups of genes that do something together, or to find out molecular similarities among a group of samples. In all respects, it is very important to know whether there is a difference between two sets of results, and whether that difference is likely to occur due to random variations of the datasets. Hence instead of simple data analysis, statistical tests are the proper way to check statistical relevance. Basically, the statistical significance test is compulsory for yielding an ordered list of relevant genes in terms of differential expression to the investigator. This article surveys a few important statistical tests which include both parametric and nonparametric tests and compares those statistical tests with respect to different performance metrics. The statistical tests have been applied on several artificial and real-life microarray gene expression datasets. Most of the time, real-life datasets contain noise and noisy data can have undesirable effect on the results of any data mining analysis. Therefore, performance analysis on noisy datasets for different statistical tests has also been performed to facilitate the comparative study. Furthermore, correlation analysis of the resultant genes from different statistical methods has been accomplished. Keywords: Analysis, correlation analysis, eigenvector, nonparametric tests, P-value, parametric tests, principal component, statistical tests.

Graph Regularized Sparsity Discriminant Analysis for face recognition

Manifold learning and Sparse Representation Classifier are two popular techniques for face recognition. Because manifold learning can find low-dimensional representations for high-dimensional data, it is widely applied in computer vision and pattern recognition. Most of the manifold learning algorithms can be unified in the graph embedding framework, where the first step is to determine the adjacent graphs. Traditional methods use k nearest neighbor or the ε-ball schemes. However, they are parametric and sensitive to noises. Moreover, it is hard to determine the size of appropriate neighborhoods. To deal with these problems, in this paper, Graph Regularized Sparsity Discriminant Analysis, GRSDA, for short, is proposed. Based on graph embedding and sparsity preserving projection, the weight matrices for intrinsic and penalty graphs are obtained through sparse representation. GRSDA seeks a subspace in which samples in intra-classes are as compact as possible while samples in inter-classes are as separable as possible. Specifically, samples in the low-dimensional space can preserve the sparse locality relationship in the same class, while enhancing the separability for samples in different classes. Hence, GRSDA can achieve better performance. Extensive experiments were carried out on ORL, YALE-B and AR face databases, and the results confirmed that the proposed algorithm outperformed LPP, UDP, SPP and DSNPE.

DETECTING DISEASED IMAGES BY USING SEMI – SUPERVISED LEARNING

The goal of semi-supervised image segmentation is to obtain the segmentation from a partially labeled image. By utilizing the image manifold structure in labeled and unlabeled pixels, semi-supervised methods propagates the user labeling to the unlabeled data, thus minimizing the need for user labeling. Several semi-supervised learning methods have been proposed in the literature. In this paper, we consider the delinquent of segmentation of large collections of images and the classification of images by allied diseases. We are detecting diseased images by the process of segmentation and classification. The segmentation used in this paper has two advantages. First, user can specify what they want by highly controlling the segmentation. Another is, at initial stage this model requires only minimum tuning of model parameters. Once initial tuning is done, the setup can be used to automatically segment a large collection of images that are distinct but share similar features. And for classification of diseases, a manifold learning method, called parameter-free semi-supervised local Fisher discriminant analysis is used. This method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, which can be computed efficiently by Eigen decomposition. Espousal experiments on various collections of biological images suggest that the proposed model is effective for segmentation with classification and is computationally efficient.

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

On the linear discriminant analysis for large number of classes

We study the challenging problem to classify samples into a large number of classes, and propose the idea of using different Dimensionality-Reduction (DR) projections for different classes of samples. Based on this intuitive idea, the traditional Linear Discriminant Analysis (LDA) and the trace-ratio LDA are formulated to their corresponding new multi-subspace objectives. We justify that certain effects of class-adaptive feature selection are naturally achieved via our multi-subspace DR methods. Experiments on seven datasets show that, our multi-subspace trace-ratio LDA outperform its ratio-trace and single-subspace counterparts, and its advantage is more apparent when the number of classes to be classified is large.

Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained.

Sample re-weighting hyper box classifier for multi-class data classification

In this work, we propose two novel classifiers for multi-class classification problems using mathematical programming optimisation techniques. A hyper box-based classifier (Xu & Papageorgiou, 2009) that iteratively constructs hyper boxes to enclose samples of different classes has been adopted. We firstly propose a new solution procedure that updates the sample weights during each iteration, which tweaks the model to favour those difficult samples in the next iteration and therefore achieves a better final solution. Through a number of real world data classification problems, we demonstrate that the proposed refined classifier results in consistently good classification performance, outperforming the original hyper box classifier and a number of other state-of-the-art classifiers.Furthermore, we introduce a simple data space partition method to reduce the computational cost of the proposed sample re-weighting hyper box classifier. The partition method partitions the original dataset into two disjoint regions, followed by training sample re-weighting hyper box classifier for each region respectively. Through some real world datasets, we demonstrate the data space partition method considerably reduces the computational cost while maintaining the level of prediction accuracies.

Data size reduction strategy for the classification of breath and air samples using multicapillary column-ion mobility spectrometry.

Ion mobility spectrometry combined with multicapillary column separation (MCC-IMS) is a well-known technology for detecting volatile organic compounds (VOCs) in gaseous samples. Due to their large data size, processing of MCC-IMS spectra is still the main bottleneck of data analysis, and there is an increasing need for data analysis strategies in which the size of MCC-IMS data is reduced to enable further analysis. In our study, the first untargeted chemometric strategy is developed and employed in the analysis of MCC-IMS spectra from 264 breath and ambient air samples. This strategy does not comprise identification of compounds as a primary step but includes several preprocessing steps and a discriminant analysis. Data size is significantly reduced in three steps. Wavelet transform, mask construction, and sparse-partial least squares-discriminant analysis (s-PLS-DA) allow data size reduction with down to 50 variables relevant to the goal of analysis. The influence and compatibility of the data reduction tools are studied by applying different settings of the developed strategy. Loss of information after preprocessing is evaluated, e.g., by comparing the performance of classification models for different classes of samples. Finally, the interpretability of the classification models is evaluated, and regions of spectra that are related to the identification of potential analytical biomarkers are successfully determined. This work will greatly enable the standardization of analytical procedures across different instrumentation types promoting the adoption of MCC-IMS technology in a wide range of diverse application fields.

Rapid discrimination of slimming capsules based on illegal additives by electronic nose and flash gas chromatography.

The discrimination of counterfeit and/or illegally manufactured medicines is an important task in the pharmaceutical industry for pharmaceutical safety. In this study, 22 slimming capsule samples with illegally added sibutramine and phenolphthalein were analyzed by electronic nose and flash gas chromatography. To reveal the difference among the different classes of samples, principal component analysis and linear discriminant analysis were employed to analyze the data acquired from electronic nose and flash gas chromatography, respectively. The samples without illegal additives can be discriminated from the ones with illegal additives by using electronic nose or flash gas chromatography data individually. To improve the performance of classification, a data fusion strategy was applied to integrate the data from electronic nose and flash gas chromatography data into a single model. The results show that the samples with phenolphthalein, sibutramine and both can be classified well by using fused data.

Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition

With the rapid development of mobile devices and pervasive computing technologies, acceleration-based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space. Therefore, we present a nonmetric scheme that retains discriminative and robust frequency domain information by developing a novel ensemble manifold rank preserving (EMRP) algorithm. EMRP simultaneously considers three aspects: 1) it encodes the local geometry using the ranking order information of intraclass samples distributed on local patches; 2) it keeps the discriminative information by maximizing the margin between samples of different classes; and 3) it finds the optimal linear combination of the alignment matrices to approximate the intrinsic manifold lied in the data. Experiments are conducted on the South China University of Technology naturalistic 3-D acceleration-based activity dataset and the naturalistic mobile-devices based human activity dataset to demonstrate the robustness and effectiveness of the new nonmetric scheme for acceleration-based human activity recognition.

Local Patch Discriminative Metric Learning for Hyperspectral Image Feature Extraction

In hyperspectral image (HSI) classification, feature extraction is one important step. Traditional methods, e.g., principal component analysis (PCA) and locality preserving projection, usually neglect the information of within-class similarity and between-class dissimilarity, which is helpful to the improvement of classification. On the other hand, most of these methods, e.g., PCA and linear discriminative analysis, consider that the HSI data lie on a low-dimensional manifold or each class is on a submanifold. However, some class data of HSI may lie on a multimanifold. To avoid these problems, we propose a method for feature extraction in HSIs, assuming that a local region resides on a submainfold. In our method, we deal with the data region by region by taking into account the different discriminative locality information. Then, under the metric learning framework, a robust distance metric is learned. It aims to learn a subspace in which the samples in the same class are as near as possible while the samples in different classes are as far as possible. Encouraging experimental results on two available hyperspectral data sets indicate that our proposed algorithm outperforms many existing feature extract methods for HSI classification.

Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities.

Cross-Sensor Iris Recognition through Kernel Learning

Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.

Multiobjective Binary Biogeography Based Optimization for Feature Selection Using Gene Expression Data

Gene expression data play an important role in the development of efficient cancer diagnoses and classification. However, gene expression data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a multi-objective biogeography based optimization method is proposed to select the small subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the Fisher-Markov selector is used to choose the 60 top gene expression data. Secondly, to make biogeography based optimization suitable for the discrete problem, binary biogeography based optimization, as called BBBO, is proposed based on a binary migration model and a binary mutation model. Then, multi-objective binary biogeography based optimization, as we called MOBBBO, is proposed by integrating the non-dominated sorting method and the crowding distance method into the BBBO framework. Finally, the MOBBBO method is used for gene selection, and support vector machine is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on ten gene expression dataset benchmarks. Experimental results demonstrate that the proposed method is better or at least comparable with previous particle swarm optimization (PSO) algorithm and support vector machine (SVM) from literature when considering the quality of the solutions obtained.

MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples

One of the significant obstacles in the development of clinically relevant microarray-derived biomarkers and classifiers is tissue heterogeneity. Physical cell separation techniques, such as cell sorting and laser-capture microdissection, can enrich samples for cell types of interest, but are costly, labor intensive and can limit investigation of important interactions between different cell types. We developed a new computational approach, called microarray microdissection with analysis of differences (MMAD), which performs microdissection in silico. Notably, MMAD (i) allows for simultaneous estimation of cell fractions and gene expression profiles of contributing cell types, (ii) adjusts for microarray normalization bias, (iii) uses the corrected Akaike information criterion during model optimization to minimize overfitting and (iv) provides mechanisms for comparing gene expression and cell fractions between samples in different classes. Computational microdissection of simulated and experimental tissue mixture datasets showed tight correlations between predicted and measured gene expression of pure tissues as well as tight correlations between reported and estimated cell fraction for each of the individual cell types. In simulation studies, MMAD showed superior ability to detect differentially expressed genes in mixed tissue samples when compared with standard metrics, including both significance analysis of microarrays and cell type-specific significance analysis of microarrays. We have developed a new computational tool called MMAD, which is capable of performing robust tissue microdissection in silico, and which can improve the detection of differentially expressed genes. MMAD software as implemented in MATLAB is publically available for download at http://sourceforge.net/projects/mmad/.

Image-to-Set Face Recognition Using Locality Repulsion Projections and Sparse Reconstruction-Based Similarity Measure

For many practical face recognition systems such as law enforcement, e-passport, and ID card identification, there is usually only a single sample per person (SSPP) enrolled in these systems, and many existing face recognition methods may fail to work well because there are not enough samples for discriminative feature extraction in this scenario. However, the probe samples of these face recognition systems are usually captured on the spot, and it is possible to collect multiple face images per person for on-location probing, which is potentially useful to improve the recognition performance. In this paper, we propose a method based on locality repulsion projections (LRP) and a sparse reconstruction-based similarity measure (SRSM) to address the problem of SSPP face recognition using multiple probe images. The LRP method is motivated by our observation that similar face images from different people may lie in a locality in the feature space and cause misclassifications. We design the method with the aim of separating the samples of different classes within a neighborhood through subspace projections for easier classification. To better characterize the similarity between each gallery face and the probe image set, we propose a SRSM method for assigning a label to each probe image set. Experimental results on five widely used face datasets are presented to demonstrate the effectiveness of the proposed approach.

Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization

Conventional visual recognition systems usually train an image classifier in a bath mode with all training data provided in advance. However, in many practical applications, only a small amount of training samples are available in the beginning and many more would come sequentially during online recognition. Because the image data characteristics could change over time, it is important for the classifier to adapt to the new data incrementally. In this paper, we present an online metric learning method to address the online scene recognition problem via adaptive similarity measurement. Given a number of labeled data followed by a sequential input of unseen testing samples, the similarity metric is learned to maximize the margin of the distance among different classes of samples. By considering the low rank constraint, our online metric learning model not only can provide competitive performance compared with the state-of-the-art methods, but also guarantees convergence. A bi-linear graph is also defined to model the pair-wise similarity, and an unseen sample is labeled depending on the graph-based label propagation, while the model can also self-update using the more confident new samples. With the ability of online learning, our methodology can well handle the large-scale streaming video data with the ability of incremental self-updating. We evaluate our model to online scene categorization and experiments on various benchmark datasets and comparisons with state-of-the-art methods demonstrate the effectiveness and efficiency of our algorithm.

Integrated Statistical and Rule-Mining Techniques for Dna Methylation and Gene Expression Data Analysis

Abstract For determination of the relationships among significant gene markers, statistical analysis and association rule mining are considered as very useful protocols. The first protocol identifies the significant differentially expressed/methylated gene markers, whereas the second one produces the interesting relationships among them across different types of samples or conditions. In this article, statistical tests and association rule mining based approaches have been used on gene expression and DNA methylation datasets for the prediction of different classes of samples (viz., Uterine Leiomyoma/class-formersmoker and uterine myometrium/class-neversmoker). A novel rule-based classifier is proposed for this purpose. Depending on sixteen different rule-interestingness measures, we have utilized a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training set of data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to estimate its class-label through weighted-sum method. We have run this classifier on the combined dataset using 4-fold cross-validations, and thereafter a comparative performance analysis has been made with other popular rulebased classifiers. Finally, the status of some important gene markers has been identified through the frequency analysis in the evolved rules for the two class-labels individually to formulate the interesting associations among them.

A multivariate analysis of protein microarrays for signature selection profiles

A multivariate analysis of protein microarrays for signature selection profiles

A semi-supervised approach for dimensionality reduction with distributional similarity

Semi-supervised learning has recently received considerable attention in machine learning. In this paper, we propose a novel diffusion maps based semi-supervised algorithm for dimensionality reduction, visualization and data representation. Unlike previous work which uses only geometric information for similarity metric construction, a distributional similarity metric is introduced to modify the geometric relationship of samples. This metric is defined using the posterior probability over the labels of each sample, which is learned through the Expectation–Maximization (EM) algorithm. The Euclidean distance between points on the intrinsic manifold learned by our proposed method is equal to the label-dependent “diffusion distance”, which is modified by the distributional similarity related metric, in the original space. Our algorithm preserves the local manifold structure in addition to separating samples in different classes, thus facilitates the classification. Encouraging experimental results on handwritten digits, Yale faces, UCI data sets and the Weizmann data set show that the algorithm can improve the classification accuracy significantly.