Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset

The growth in internet traffic volume presents a new issue in anomaly detection, one of which is the high data dimension. The feature selection technique has been proven to be able to solve the problem of high data dimension by producing relevant features. On the other hand, high-class imbalance is a problem in feature selection. In this study, two feature selection approaches are proposed that are able to produce the most ideal features in the high-class imbalanced dataset. CICIDS-2017 is a reliable dataset that has a problem in high-class imbalance, therefore it is used in this study. Furthermore, this study performs experiments in Information Gain feature selection technique on the imbalance class datasaet. For validation, the Random Forest classification algorithm is used, because of its ability to handle multi-class data. The experimental results show that the proposed approaches have a very surprising performance, and surpass the state-of-the-art methods

Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis

<p>Abstract</p> <p>Background</p> <p>Dimension reduction is a critical issue in the analysis of microarray data, because the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers. It consists of two types of methods, i.e. feature selection and feature extraction. Principle component analysis (PCA) and partial least squares (PLS) are two frequently used feature extraction methods, and in the previous works, the top several components of PCA or PLS are selected for modeling according to the descending order of eigenvalues. While in this paper, we prove that not all the top features are useful, but features should be selected from all the components by feature selection methods.</p> <p>Results</p> <p>We demonstrate a framework for selecting feature subsets from all the newly extracted components, leading to reduced classification error rates on the gene expression microarray data. Here we have considered both an unsupervised method PCA and a supervised method PLS for extracting new components, genetic algorithms for feature selection, and support vector machines and <it>k </it>nearest neighbor for classification. Experimental results illustrate that our proposed framework is effective to select feature subsets and to reduce classification error rates.</p> <p>Conclusion</p> <p>Not only the top features newly extracted by PCA or PLS are important, therefore, feature selection should be performed to select subsets from new features to improve generalization performance of classifiers.</p

An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification

In predictive tasks, real-world datasets often present di erent degrees of imbalanced (i.e., long-tailed or skewed) distributions. While the majority (the head or the most frequent) classes have su cient samples, the minority (the tail or the less frequent or rare) classes can be under-represented by a rather limited number of samples. Data pre-processing has been shown to be very e ective in dealing with such problems. On one hand, data re-sampling is a common approach to tackling class imbalance. On the other hand, dimension reduction, which reduces the feature space, is a conventional technique for reducing noise and inconsistencies in a dataset. However, the possible synergy between feature selection and data re-sampling for high-performance imbalance classification has rarely been investigated before. To address this issue, we carry out a comprehensive empirical study on the joint influence of feature selection and re-sampling on two-class imbalance classification. Specifically, we study the performance of two opposite pipelines for imbalance classification by applying feature selection before or after data re-sampling. We conduct a large number of experiments, with a total of 9225 tests, on 52 publicly available datasets, using 9 feature selection methods, 6 resampling approaches for class imbalance learning, and 3 well-known classification algorithms. Experimental results show that there is no constant winner between the two pipelines; thus both of them should be considered to derive the best performing model for imbalance classification. We find that the performance of an imbalance classification model not only depends on the classifier adopted and the ratio between the number of majority and minority samples, but also depends on the ratio between the number of samples and features. Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.TIN2017-89517-

Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification

Gene expression data are usually known for having a large number of features. Usually, some of these features are irrelevant and redundant. However, in some cases, all features, despite being numerous, show high importance and contribute to the data analysis. In a similar fashion, gene expression data sometimes have limited instances with a high rate of imbalance among the classes. This can limit the exposure of a classification model to instances of different categories, thereby influencing the performance of the model. In this study, we proposed a cancer detection approach that utilized data preprocessing techniques such as oversampling, feature selection, and classification models. The study used SVMSMOTE for the oversampling of the six examined datasets. Further, we examined different techniques for feature selection using dimension reduction methods and classifier-based feature ranking and selection. We trained six machine learning algorithms, using repeated 5-fold cross-validation on different microarray datasets. The performance of the algorithms differed based on the data and feature reduction technique used

Latent Similarity Identifies Important Functional Connections for Phenotype Prediction

Objective: Endophenotypes such as brain age and fluid intelligence are important biomarkers of disease status. However, brain imaging studies to identify these biomarkers often encounter limited numbers of subjects and high dimensional imaging features, hindering reproducibility. Therefore, we develop an interpretable, multivariate classification/regression algorithm, called Latent Similarity (LatSim), suitable for small sample size, high feature dimension datasets. Methods: LatSim combines metric learning with a kernel similarity function and softmax aggregation to identify task-related similarities between subjects. Inter-subject similarity is utilized to improve performance on three prediction tasks using multi-paradigm fMRI data. A greedy selection algorithm, made possible by LatSim's computational efficiency, is developed as an interpretability method. Results: LatSim achieved significantly higher predictive accuracy at small sample sizes on the Philadelphia Neurodevelopmental Cohort (PNC) dataset. Connections identified by LatSim gave superior discriminative power compared to those identified by other methods. We identified 4 functional brain networks enriched in connections for predicting brain age, sex, and intelligence. Conclusion: We find that most information for a predictive task comes from only a few (1-5) connections. Additionally, we find that the default mode network is over-represented in the top connections of all predictive tasks. Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data. Our work should lead to new insights in both algorithm design and neuroscience research. Code and demo are available at https://github.com/aorliche/LatentSimilarity/.Comment: 12 page

Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery

<p>Abstract</p> <p>Background</p> <p>As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. However, its clinical use is not fully validated yet. An important factor to prevent this young technology to become a mainstream cancer diagnostic paradigm is that robustly identifying cancer molecular patterns from high-dimensional protein expression data is still a challenge in machine learning and oncology research. As a well-established dimension reduction technique, PCA is widely integrated in pattern recognition analysis to discover cancer molecular patterns. However, its global feature selection mechanism prevents it from capturing local features. This may lead to difficulty in achieving high-performance proteomic pattern discovery, because only features interpreting global data behavior are used to train a learning machine.</p> <p>Methods</p> <p>In this study, we develop a nonnegative principal component analysis algorithm and present a nonnegative principal component analysis based support vector machine algorithm with sparse coding to conduct a high-performance proteomic pattern classification. Moreover, we also propose a nonnegative principal component analysis based filter-wrapper biomarker capturing algorithm for mass spectral serum profiles.</p> <p>Results</p> <p>We demonstrate the superiority of the proposed algorithm by comparison with six peer algorithms on four benchmark datasets. Moreover, we illustrate that nonnegative principal component analysis can be effectively used to capture meaningful biomarkers.</p> <p>Conclusion</p> <p>Our analysis suggests that nonnegative principal component analysis effectively conduct local feature selection for mass spectral profiles and contribute to improving sensitivities and specificities in the following classification, and meaningful biomarker discovery.</p

A classification for complex imbalanced data

Doctor of PhilosophyDepartment of StatisticsWei-Wen HsuWeixing SongImbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification methods, such as logistic regression and support vector machine (SVM), often perform poorly when a severely skewed class distribution is observed, not to mention under a high-dimensional longitudinal data structure. Given the ubiquity of big data in areas including modern health research, face recognition, and object identification, it is expected that imbalanced classification may encounter an additional level of difficulty that is imposed by such a complex data structure. In this dissertation, a nonparametric classification approach has been proposed for binary imbalanced data in longitudinal and high-dimensional settings. Technically, the proposed approach involves two stages. The functional principal component analysis (FPCA) is first applied for feature extraction under the sparse and irregular longitudinal data structure. The proposed univariate exponential loss function coupled with group LASSO penalty is then adopted into the classification procedure in high-dimensional settings. Along with the improvement in AUC and sensitivity under imbalanced classification, the proposed approach also provides a meaningful feature selection for interpretation while enjoying a remarkable computational efficiency. Finally, the proposed method is illustrated with the real data of Alzheimer’s disease, Pima Indians diabetes and Phoneme, and its empirical performance in finite sample size is extensively evaluated by simulations. Furthermore, the proposed method has been extended to multi-class scenario for which those aforementioned complications become more challenging. To accommodate the dense longitudinal/functional data, the use of natural cubic spline is adopted for feature extraction and dimension reduction, instead of using the FPCA. Functional biomarkers are efficiently characterized by spline coefficients which are treated as features for subsequent classification procedure. With these transformed features, a novel exponential loss function is then proposed to cast the multi-class classification task as a single optimization problem. Coupled with the group LASSO penalty, the proposed approach is also capable of performing variable selection for each class individually. Besides that, a simple weight-adjusted margin can be easily incorporated into the proposed loss function to address the issue of imbalance in multi-class data. The overall empirical performance of the proposed framework is evaluated by simulations in both high- and low-dimensional settings. Finally, the proposed multi-class classification framework is illustrated using real data of Alzheimer’s disease, gene expression, and human walking

Graph-based Data Modeling and Analysis for Data Fusion in Remote Sensing

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a computational complexity that grows exponentially with the dimension. Besides, a HSI dataset may contain a limited number of degrees of freedom due to the high correlations between data points and among the spectra. On the other hand, merely taking advantage of the sampled spectrum of individual HSI data point may produce inaccurate results due to the mixed nature of raw HSI data, such as mixed pixels, optical interferences and etc. Fusion strategies are widely adopted in data processing to achieve better performance, especially in the field of classification and clustering. There are mainly three types of fusion strategies, namely low-level data fusion, intermediate-level feature fusion, and high-level decision fusion. Low-level data fusion combines multi-source data that is expected to be complementary or cooperative. Intermediate-level feature fusion aims at selection and combination of features to remove redundant information. Decision level fusion exploits a set of classifiers to provide more accurate results. The fusion strategies have wide applications including HSI data processing. With the fast development of multiple remote sensing modalities, e.g. Very High Resolution (VHR) optical sensors, LiDAR, etc., fusion of multi-source data can in principal produce more detailed information than each single source. On the other hand, besides the abundant spectral information contained in HSI data, features such as texture and shape may be employed to represent data points from a spatial perspective. Furthermore, feature fusion also includes the strategy of removing redundant and noisy features in the dataset. One of the major problems in machine learning and pattern recognition is to develop appropriate representations for complex nonlinear data. In HSI processing, a particular data point is usually described as a vector with coordinates corresponding to the intensities measured in the spectral bands. This vector representation permits the application of linear and nonlinear transformations with linear algebra to find an alternative representation of the data. More generally, HSI is multi-dimensional in nature and the vector representation may lose the contextual correlations. Tensor representation provides a more sophisticated modeling technique and a higher-order generalization to linear subspace analysis. In graph theory, data points can be generalized as nodes with connectivities measured from the proximity of a local neighborhood. The graph-based framework efficiently characterizes the relationships among the data and allows for convenient mathematical manipulation in many applications, such as data clustering, feature extraction, feature selection and data alignment. In this thesis, graph-based approaches applied in the field of multi-source feature and data fusion in remote sensing area are explored. We will mainly investigate the fusion of spatial, spectral and LiDAR information with linear and multilinear algebra under graph-based framework for data clustering and classification problems

An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data

Feature selection has been studied widely in the literature. However, the efficacy of the selection criteria for low sample size applications is neglected in most cases. Most of the existing feature selection criteria are based on the sample similarity. However, the distance measures become insignificant for high dimensional low sample size (HDLSS) data. Moreover, the variance of a feature with a few samples is pointless unless it represents the data distribution efficiently. Instead of looking at the samples in groups, we evaluate their efficiency based on pairwise fashion. In our investigation, we noticed that considering a pair of samples at a time and selecting the features that bring them closer or put them far away is a better choice for feature selection. Experimental results on benchmark data sets demonstrate the effectiveness of the proposed method with low sample size, which outperforms many other state-of-the-art feature selection methods.Comment: European Signal Processing Conference 201