An hybrid metaheuristic approach for efficient feature selection

Network traffic in cloud computing is characterized by a large volume of data, with potential high dimensionality and high levels of redundancy. “Big data” not only retard the execution process of intrusion detection systems (IDS) but they can also lead to unsatisfactory classification accuracy. The efficient correlation-based feature selection (ECOFS) approach proposed in this paper, can handle linearly and nonlinearly dependent data and it can eliminate redundant and irrelevant features. Its effectiveness has been evaluated through its employment in an intrusion detection system. A Libsvm-IDS has been built to operate using the features selected by the proposed ECOFS algorithm. The performance of the hybrid Libsvm-IDS + ECOFS approach has been evaluated using two well-known intrusion detection evaluation datasets, namely the KDD Cup 99 and the NSL-KDD. The evaluation results show that our ECOFS algorithm selects the smallest number of features, resulting in the lowest computational cost for the Libsvm-IDS, without any performance compromise. In fact, our algorithm has achieved higher accuracy compared with two well established methods.

<p>Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. There are two types of feature selection approaches, i.e. wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm to evaluate the goodness of the features during the feature selection process while filters are independent of any classification algorithm. Feature selection is a difficult task because of feature interactions and the large search space. Existing feature selection methods suffer from different problems, such as stagnation in local optima and high computational cost. Evolutionary computation (EC) techniques are well-known global search algorithms. Particle swarm optimisation (PSO) is an EC technique that is computationally less expensive and can converge faster than other methods. PSO has been successfully applied to many areas, but its potential for feature selection has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for feature selection to select a smaller number of features and achieve similar or better classification performance than using all features. This thesis investigates the use of PSO for both wrapper and filter, and for both single objective and multi-objective feature selection, and also investigates the differences between wrappers and filters. This thesis proposes a new PSO based wrapper, single objective feature selection approach by developing new initialisation and updating mechanisms. The results show that by considering the number of features in the initialisation and updating procedures, the new algorithm can improve the classification performance, reduce the number of features and decrease computational time. This thesis develops the first PSO based wrapper multi-objective feature selection approach, which aims to maximise the classification accuracy and simultaneously minimise the number of features. The results show that the proposed multi-objective algorithm can obtain more and better feature subsets than single objective algorithms, and outperform other well-known EC based multi-objective feature selection algorithms. This thesis develops a filter, single objective feature selection approach based on PSO and information theory. Two measures are proposed to evaluate the relevance of the selected features based on each pair of features and a group of features, respectively. The results show that PSO and information based algorithms can successfully address feature selection tasks. The group based method achieves higher classification accuracies, but the pair based method is faster and selects smaller feature subsets. This thesis proposes the first PSO based multi-objective filter feature selection approach using information based measures. This work is also the first work using other two well-known multi-objective EC algorithms in filter feature selection, which are also used to compare the performance of the PSO based approach. The results show that the PSO based multiobjective filter approach can successfully address feature selection problems, outperform single objective filter algorithms and achieve better classification performance than other multi-objective algorithms. This thesis investigates the difference between wrapper and filter approaches in terms of the classification performance and computational time, and also examines the generality of wrappers. The results show that wrappers generally achieve better or similar classification performance than filters, but do not always need longer computational time than filters. The results also show that wrappers built with simple classification algorithms can be general to other classification algorithms.</p>

The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besides, several machine learning models rely on powerful feature extraction and feature selection techniques for their success. In this paper, a big data classification approach is developed using an optimized deep learning classifier integrated with hybrid feature extraction and feature selection approaches. The proposed technique uses local linear embedding-based kernel principal component analysis and perturbation theory, respectively, to extract more representative data and select the appropriate features from the big data environment. In addition, the feature selection task is fine-tuned by using perturbation theory through heuristic search based on their output accuracy. This feature selection heuristic search method is analysed with five recent heuristic optimization algorithms for deciding the final feature subset. Finally, the data are categorized through an attention-based bidirectional long short-term memory classifier that is optimized with a golden eagle-inspired algorithm. The performance of the proposed model is experimentally verified on publicly accessible datasets. From the experimental outcomes, it is demonstrated that the proposed framework is capable of classifying large datasets with more than 90% accuracy.

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

Feature selection is one of the most important machine learning procedure, and it has been successfully applied to make a preprocessing before using classification and clustering methods. High-dimensional features often appear in big data, and it’s characters block data processing. So spectral feature selection algorithms have been increasing attention by researchers. However, most feature selection methods, they consider these tasks as two steps, learn similarity matrix from original feature space (may be include redundancy for all features), and then conduct data clustering. Due to these limitations, they do not get good performance on classification and clustering tasks in big data processing applications. To address this problem, we propose an Unsupervised Feature Selection method with graph learning framework, which can reduce the redundancy features influence and utilize a low-rank constraint on the weight matrix simultaneously. More importantly, we design a new objective function to handle this problem. We evaluate our approach by six benchmark datasets. And all empirical classification results show that our new approach outperforms state-of-the-art feature selection approaches.

In today’s digital era, e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices, computers and the internet to provide high-quality healthcare services. E-healthcare decision support systems have been developed to optimize the healthcare services and enhance a patient’s health. These systems enable rapid access to the specialized healthcare services via reliable information, retrieved from the cases or the patient histories. This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions. In the current research work, a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System (SFLODL-DSS) is designed for the diagnosis of the Cardiovascular Diseases (CVD). The aim of the proposed model is to identify and classify the cardiovascular diseases. The proposed SFLODL-DSS technique primarily incorporates the SFLO-based Feature Selection (SFLO-FS) approach for feature subset election. For the purpose of classification, the Autoencoder with Gated Recurrent Unit (AEGRU) model is exploited. Finally, the Bacterial Foraging Optimization (BFO) algorithm is employed to fine-tune the hyperparameters involved in the AEGRU method. To demonstrate the enhanced performance of the proposed SFLODL-DSS technique, a series of simulations was conducted. The simulation outcomes established the superiority of the proposed SFLODL-DSS technique as it achieved the highest accuracy of 98.36%. Thus, the proposed SFLODL-DSS technique can be exploited as a proficient tool in the future for the detection and classification of CVD.

Nowadays, many disciplines have to deal with big datasets that additionally involve a high number of features. Feature selection methods aim at eliminating noisy, redundant, or irrelevant features that may deteriorate the classification performance. However, traditional methods lack enough scalability to cope with datasets of millions of instances and extract successful results in a delimited time. This paper presents a feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets. The algorithm decomposes the original dataset in blocks of instances to learn from them in the map phase; then, the reduce phase merges the obtained partial results into a final vector of feature weights, which allows a flexible application of the feature selection procedure using a threshold to determine the selected subset of features. The feature selection method is evaluated by using three well-known classifiers (SVM, Logistic Regression, and Naive Bayes) implemented within the Spark framework to address big data problems. In the experiments, datasets up to 67 millions of instances and up to 2000 attributes have been managed, showing that this is a suitable framework to perform evolutionary feature selection, improving both the classification accuracy and its runtime when dealing with big data problems.

In the field of data processing and analysis, the dataset may be a large set of features that restrict data usability and applicability, and thus the dimensions of data sets need to be reduced. Feature selection is the process of removing as much of the redundant and irrelevant features as possible from the original dataset to improve the mining process efficiency. This paper presented a study to evaluate and compare the effect of filter and wrapper methods as feature selection approaches in terms of classification accuracy and time complexity. The Naive Bayes Classifier and three classification datasets from the UCI repository are utilizing in the classification procedure. To investigate the effect of feature selection methods, they are applied to the different characteristics datasets to obtain the selected feature vectors which are then classified according to each dataset category. The datasets used in this paper are the Iris, Ionosphere, and Ovarian Cancer dataset. Experimental results indicate that the filter and wrapper methods provide approximately equal classification accuracy where the average accuracy value of the Ionosphere and Ovarian Cancer dataset is 0.78 and 0.91 for the same selected feature vectors respectively. For Iris dataset, the filter method outperforms the wrapper method by achieving the same accuracy value using only half number of selected features. The results also show that the filter method surpasses when considering the execution time.

Feature selection is a dimensionality reduction method known as a main step in data mining and machine learning. The aim of feature selection is to remove redundant and unrelated features. In recent years several feature selection methods based on graph theory and social networking techniques have been proposed .In this study, a feature selection approach based on multi objective PSO algorithm and social network techniques is presented. In the proposed method, Fisher score, node centrality and edge centrality are used to construct the fitness function in order to present a multi objective particle swarm optimization (PSO) approach. The proposed method run over a variety of datasets and the results are compared with the well-known filter-based feature selection methods. The results show that the proposed method is effective and the performance of the proposed method better than other methods in some cases.

As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.

Retrieval of crop biophysical-biochemical variables from airborne AVIRIS-NG data using hybrid inversion of PROSAIL-D

Feature selection (FS) has been studied as a multi-objective problem in recent years to improve the classification effect. In this paper, to expand the ability of Bacterial Foraging Optimization (BFO) in feature selection problems for classification, a Multi-objective Adapting Chemotaxis Bacterial Foraging Optimization (abbreviated as MOACBFO) is proposed. In MOACBFO, a structural variation strategic model is proposed to reduce the computational complexity for multi-objective problems and applied with a dynamically updated external matrix to record the performance of bacteria on two objectives. In addition, an adaptive chemotaxis step mechanism is designed to help the bacteria jump out of the local optimality. To further enhance the diversity of bacteria searching capability, a feature subset updating strategy is developed. The optimal feature subset and fitness value are stored in the external matrix continuously by comparing them with the historical value records. The performance of the proposed algorithm is demonstrated by comparing it with four other advanced swarm intelligent algorithms on 11 high-dimensional microarray datasets. The results indicate that the MOACBFO performs better in achieving a lower classification error rate using a small number of features.

Generally, there are two types of approaches available for the detection of networks attacks, namely signature based and anomaly based. In this work, we will analyze the performance of anomaly-based detection model in SDN with the help of some common machine learning algorithms and feature selection mechanisms. We construct a mechanism of machine learning model for an intrusion detection system and train the model with the NSL-KDD Data set using feature selection technique. In order to enhance the performance of the classifier, some feature selection methods have been applied as a prepossessing of the data set. We have used five feature selection methods, namely Info Gain, Gain Ratio, CFS Subset Evaluator, Symmetric Uncertainty, and Chi-square test. A full data set of 41 features and a reduced data set after applying feature selection method has been experimented. A data set with feature selection ensures the highest accuracy with Random Forest classifier using Gain Ratio feature selection Evaluator.

High-feature dimensions is one of the obvious challenges facing large-scale hierarchical classification problem. One promising way to overcome such problem is reducing the features size by removing irrelevant, noise and redundant features. Filter-based feature selection methods are commonly used to construct smaller subset of most relevant features in either adaptive or global approach at each non leaf node within the hierarchy. Both the Feature selection approaches do not only reduce time and space complexity but also improve classification accuracy of the learning models. In this study, the effectiveness of the main approaches for constructing relevant features subset, that is adaptive and global approaches are analyzed using the two filter-based methods suitable for text classification. Experiment evaluation conducted on 20NG, IPC, and DMOZ-small datasets shows that implementing feature selection adaptively has more capability of selecting most relevant features, thus, achieving the best classification accuracy. Moreover, further evaluation shown integrating feature selection with LSHC records improve performance when compared to without feature selection.

In this work, we propose a novel Feature Selection framework, called Sparse-Modeling Based Approach for Class Specific Feature Selection (SMBA-CSFS), that simultaneously exploits the idea of Sparse Modeling and Class-Specific Feature Selection. Feature selection plays a key role in several fields (e.g., computational biology), making it possible to treat models with fewer variables which, in turn, are easier to explain, by providing valuable insights on the importance of their role, and might speed the experimental validation up. Unfortunately, also corroborated by the no free lunch theorems, none of the approaches in literature is the most apt to detect the optimal feature subset for building a final model, thus it still represents a challenge. The proposed feature selection procedure conceives a two steps approach: (a) a sparse modeling-based learning technique is first used to find the best subset of features, for each class of a training set; (b) the discovered feature subsets are then fed to a class-specific feature selection scheme, in order to assess the effectiveness of the selected features in classification tasks. To this end, an ensemble of classifiers is built, where each classifier is trained on its own feature subset discovered in the previous phase, and a proper decision rule is adopted to compute the ensemble responses. In order to evaluate the performance of the proposed method, extensive experiments have been performed on publicly available datasets, in particular belonging to the computational biology field where feature selection is indispensable: the acute lymphoblastic leukemia and acute myeloid leukemia, the human carcinomas, the human lung carcinomas, the diffuse large B-cell lymphoma, and the malignant glioma. SMBA-CSFS is able to identify/retrieve the most representative features that maximize the classification accuracy. With top 20 and 80 features, SMBA-CSFS exhibits a promising performance when compared to its competitors from literature, on all considered datasets, especially those with a higher number of features. Experiments show that the proposed approach might outperform the state-of-the-art methods when the number of features is high. For this reason, the introduced approach proposes itself for selection and classification of data with a large number of features and classes.