Optimization Based Feature Selection Research Articles

Multi-Objective Particle Swarm Optimization-based Feature Selection for Face Recognition

Multi-Objective Particle Swarm Optimization-based Feature Selection for Face Recognition

A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection

Survey is one of the crucial data retrieval methods in the literature. However, surveys often contain missing data and redundant features. Therefore, missing feature completion and feature selection have been widely used for knowledge extraction from surveys. We have a hypothesis to solve these two problems. To implement our hypothesis, a classification method is presented. Our proposed method consists of missing feature completion with a statistical moment (average) and feature selection using a novel swarm optimization method. Firstly, an average based supervised feature completion method is applied to Hepatocellular Carcinoma survey (HCC). The used HCC survey consists of 49 features. To select meaningful features, a chaotic Darcy optimization based feature selection method is presented and this method selects 31 most discriminative features of the completed HCC dataset. 0.9879 accuracy rate was obtained by using the proposed chaotic Darcy optimization-based HCC survival classification method.

Particle Swarm Optimisation Based Feature Selection for Software Effort Prediction Using Supervised Machine Learning and Ensemble Methods: A Comparative Study

An essential goal of software cost prediction is predicting accurate costs, which is a vital part of the software process. Individuals with various skills carry out most software development tasks, so efforts are measured by person-months and time. The overestimation and underestimation of developing costs is a future disappointment. Additionally, software venture managers will determine the cost of software development. The prevailing expense for any software is the cost of ascertaining efforts, making software cost estimation a crucial task. The past three decades have seen various approaches to software cost estimations based on software metrics. However, it is difficult to decide which model offers a more precise estimate on the data set. This article uses three different data sets: Albrecht, Desharnais and COCOMONASA. Classification often includes many features in a data set, but not all are useful for classification. Abusive and repetitive symptoms reduce performance, so this article uses particle swarm optimisation as a feature selection algorithm for different machine learning methods like Random Forest, REPTree, SMOReg, Linear Regression, M5Rule and MLP. At the same time, bagging is also used with said base learners. The bagging M5Rule gives the best performance with an MMRE value of 28.78%.

Predictive Modeling of Type 1 Diabetes Stages Using Disparate Data Sources.

This study aims to model genetic, immunologic, metabolomics, and proteomic biomarkers for development of islet autoimmunity (IA) and progression to type 1 diabetes in a prospective high-risk cohort. We studied 67 children: 42 who developed IA (20 of 42 progressed to diabetes) and 25 control subjects matched for sex and age. Biomarkers were assessed at four time points: earliest available sample, just prior to IA, just after IA, and just prior to diabetes onset. Predictors of IA and progression to diabetes were identified across disparate sources using an integrative machine learning algorithm and optimization-based feature selection. Our integrative approach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV). Among the strongest predictors of IA were change in serum ascorbate, 3-methyl-oxobutyrate, and the PTPN22 (rs2476601) polymorphism. Serum glucose, ADP fibrinogen, and mannose were among the strongest predictors of progression to diabetes. This proof-of-principle analysis is the first study to integrate large, diverse biomarker data sets into a limited number of features, highlighting differences in pathways leading to IA from those predicting progression to diabetes. Integrated models, if validated in independent populations, could provide novel clues concerning the pathways leading to IA and type 1 diabetes.

A modified grey wolf optimization based feature selection method from EEG for silent speech classification

Brain computer interfaces (BCI’s) employing electroencephalographic signals are being applied to a wide variety of applications like motor imagery task classification, prosthetics etc. Electroencephalography (EEG) data are inherently non-stationary and noisy, and as such identification of appropriate features for classification is a crucial task. Selection of features based on genetic algorithms (GA) has been applied, but it leads to a redundant set of features. In the present work, grey wolf optimization (GWO) based feature selection method has been applied on EEG data for silent speech classification. The EEG data from the ABISSR (Analysis of Brain Waves and development of intelligent model for silent speech recognition) project was used in the proposed work. An accuracy of 65% was obtained in classifying five imagined vowels /a/, /e/, /i/, /o/ and /u/ from EEG data using support vector machine (SVM). Moreover, it was observed that the GWO outperformed GA in optimization.

An Adaptive Intrusion Detection Scheme for Cloud Computing

To provide dynamic resource management, live virtual machine migration is used to move a virtual machine from one host to another. However, virtual machine migration poses challenges to cloud intrusion detection systems because movement of VMs from one host to another makes it difficult to create a consistent normal profile for anomaly detection. Hence, there is a need to provide an adaptive anomaly detection system capable of adapting to changes that occur in the cloud data during VM migration. To achieve this, the authors proposed a scheme for adaptive IDS for Cloud computing. The proposed adaptive scheme is comprised of four components: an ant colony optimization-based feature selection component, a statistical time series change point detection component, adaptive classification, and model update component, and a detection component. The proposed adaptive scheme was evaluated using simulated datasets collected from vSphere and performance comparison shows improved performance over existing techniques.

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

Twitter sentiment analysis is a vital concept in determining the public opinions about products, services, events or personality. Analyzing the medical tweets on a specific topic can provide immense benefits in medical industry. However, the medical tweets require efficient feature selection approach to produce significantly accurate results. Penguin search optimization algorithm (PeSOA) has the ability to resolve NP-hard problems. This paper aims at developing an automated opinion mining framework by modeling the feature selection problem as NP-hard optimization problem and using PeSOA based feature selection approach to solve it. Initially, the medical tweets based on cancer and drugs keywords are extracted and pre-processed to filter the relevant informative tweets. Then the features are extracted based on the Natural Language Processing (NLP) concepts and the optimal features are selected using PeSOA whose results are fed as input to three baseline classifiers to achieve optimal and accurate sentiment classification. The experimental results obtained through MATLAB simulations on cancer and drug tweets using k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) indicate that the proposed PeSOA feature selection based tweet opinion mining has improved the classification performance significantly. It shows that the PeSOA feature selection with the SVM classifier provides superior sentiment classification than the other classifiers.

Particle Swarm Optimization based Feature Selection with Evolutionary Outlay-Aware Deep Belief Network Classifier (PSO-EOA-DBNC) for High Dimensional Datasets

Particle Swarm Optimization based Feature Selection with Evolutionary Outlay-Aware Deep Belief Network Classifier (PSO-EOA-DBNC) for High Dimensional Datasets

Building Large Scale Cloud System for Product Sentiment Analysis using Hybrid Group Search Optimization Based Feature Selection

A very powerful technology that performs complex computing in a massive scale is known as Cloud computing. There has been a massive growth that has been observed in the data scale which may also be big data which is generated by means of cloud computing which is observed. Sentiment Analysis, on the other hand, denotes the opinion extraction of users from the documents used for review. A sentiment classification that makes use of methods of Machine Learning (ML) can face problems in high dimensionality for a feature vector. Thus, the method of feature selection is needed for the elimination of all noisy and irrelevant features from a feature vector for efficiently working the ML algorithms. All chosen features will be sub-optimal owing to a Non-Deterministic Polynomial (NP) hard type of technique that was used. The Group Search Optimization (GSO) based algorithm which was on the basis of a method of feature selection will find some optimal feature subsets through the elimination of all redundant features. For this work, the method of feature selection based on the GSO was applied to the sentiment classification. There was also a method of feature selection which was hybrid and based on the GSO and Local Beam Search (LBS) that has been proposed for a sentiment classification. The methods proposed were evaluated based on the product review dataset of Amazon. The results of the experiment proved that this method of a hybrid feature selection can outperform all other methods of feature selection for a sentiment classification.

Extreme Learning Machine for Thyroid Nodule Classification with Graph Cluster Ant Colony Optimization Based Feature Selection

Thyroid nodule is defined as an endocrine malignancy that occurs in humans due to abnormal growth of cells. Recently, an increasing level of thyroid incidence has been identified worldwide. Thus, it is necessary to detect the nodules at an early stage. Ultrasonography is an important tool that is utilized for the detection as well as differentiation of malignant thyroid nodules from benign nodules. The nodules in ultrasound appear in different heterogenic forms, which are difficult to differentiate by the physicians. Further, large number of features available in US characteristics increases the computation time as well as complexity of classification. In this paper, Graph-Clustering Ant Colony Optimization based Extreme Learning Machine approach is proposed to achieve efficient diagnosis of thyroid nodules. It will enhance thyroid nodule classification by selecting only the optimal features and further using it for improving the function of classifier. The main goal of this technique is to differentiate the malignant nodules from the benign nodules. The performance of both feature selection and classification are evaluated through parameters such as accuracy, AUC, sensitivity and specificity. From the experimental results, it is revealed that the proposed method is significantly better than the existing methods. Thus, it is considered to be an effective tool for diagnosing the thyroid nodules with less complexity and reduced computation time.

Sparsity-regularized feature selection for multi-class remote sensing image classification

Remote sensing image classification plays an important role in a wide range of applications and has caused widely concerns. During the last few years, great efforts have been made to develop a number of scene classification methods for remote sensing images. However, the existing remote sensing image classification methods do not perform satisfactorily in dealing with multi-class classification problems and rely heavily on the quality of data sets. These disadvantages seriously restrict the application of remote sensing image, including industrial research, analysis and calculation of land use and land coverage. To this end, this paper proposes a remote sensing image classification algorithm based on the sparse regularized feature learning method. Specifically, after constructing bag of features by using speeded up robust features extraction algorithm, direct sparsity optimization-based feature selection method is applied for selecting discriminative features, which is used for constructing support vector machine classifier model. The proposed algorithm has been evaluated and compared with other advanced feature selection methods on four public remote sensing image data sets. The experimental results demonstrate the effectiveness of our proposed image classification algorithm, which has been successfully applied to remote sensing image classification tasks.

Ant Colony Optimization Based Subset Feature Selection in Speech Processing: Constructing Graphs with Degree Sequences

Feature selection or the process of selecting the most discriminating feature subset is an essential practice in speech processing that significantly affects the performance of classification. However, the volume of features that presents in speech processing makes the feature selection perplexing. Moreover, finding the optimal feature subset is a NP-hard problem (2n). Thus, a good searching strategy is required to avoid evaluating large number of combinations in the whole feature subsets. As a result, in recent years, many heuristic based search algorithms are developed to address this NP-hard problem. One of the several meta heuristic algorithms that is applied in many application domains to solve feature selection problem is Ant Colony Optimization (ACO) based algorithms. ACO based algorithms are nature-inspired from the foraging behavior of actual ants. The success of an ACO based feature selection algorithm depends on the choice of the construction graph with respect to runtime behavior. While most ACO based feature selection algorithms use fully connected graphs, this paper proposes ACO based algorithm that uses graphs with prescribed degree sequences. In this method, the degree of the graph representing the search space will be predicted and the construction graph that satisfies the predicted degree will be generated. This research direction on graph representation for ACO algorithms may offer possibilities to reduce computation complexity from O(n2) to O(nm) in which m is the number of edges. This paper outlines some popular optimization based feature selection algorithms in the field of speech processing applications and overviewed ACO algorithm and its main variants. In addition to that, ACO based feature selection is explained and its application in various speech processing tasks is reviewed. Finally, a degree based graph construction for ACO algorithms is proposed.

Improved Ant Colony on Feature Selection and Weighted Ensemble to Neural Network Based Multimodal Disease Risk Prediction (WENN-MDRP) Classifier for Disease Prediction Over Big Data

As the big data is growing in biomedical and healthcare communities, so are precise analyses of medical data aids, premature disease identification, patient care as well as community services. On the other hand, the accuracy of the analysis decreases, if the medical data quality is imperfect. As a result, the choice of features from the dataset turns out to be an extremely significant task. Feature selection has exposed its efficiency in numerous applications by means of constructing modest and more comprehensive models, enlightening learning performance and preparing clean and clear data. The proposed method analyzes the difficulties of feature selection for big data analytics. Improved Ant Colony Optimization based Feature Selection (IACO) algorithm is presented for resolving this issue. The reconstruction of missing data before the incomplete data available was performed with help of latent factor mode. Therefore, it was not easy to choose the best features from the structured and unstructured data. the unheard technique which is called Weighted Ensemble Based Neural Network for multimodal disease risk prediction(WENN-MDRP) algorithm is implemented in order to provide the best features selection among structured as well as unstructured data. The research method provides improved prediction accuracy when matched with conventional techniques. In the MATLAB environment, the presented classifiers are implemented. The outcomes are computed in regard to recall, precision, accuracy, f-measure and error rate.

Apply Particle Swarm Optimization Algorithm to Measure the Software Quality

The process of improvement software quality began from early stages of software engineering development. It uses multiple quality metrics which are very important in software development. To calculate the standards quality of in software testing has been adopted. The software testing is focusing on the Software defect. In this paper is proposed new methods which combine the particle swarm optimization (PSO) to handle the best features Extraction with back-propagation networks to testing and evaluation of the data set . The paper depended database for NASA standards data. The result and experiment method improved quality performance for all classification methods used in the research"Combining Particle Swarm Optimization based Feature Selection and Bagging for Software Defect Prediction ".

Mammogram classification using contourlet features with forest optimization-based feature selection approach

Breast cancer continues to be one of the major health issues across the world and it is mostly observed in females. However, the actual cause of this cancer is still an ongoing research topic. Hence, early detection and diagnosis of breast cancer are considered to be an effective and reliable solution. Mammography is one of the most efficacious medical tools for early detection of breast cancer. The radiologists identify the suspicious regions in the breast by carefully examining the mammograms. However, mammograms are sometimes difficult to analyze when the breast tissues are dense. Therefore, a computer-aided diagnosis (CAD) system is adopted which can improve the decisions of the radiologists. This paper proposes a hybrid CAD framework to classify the suspicious regions into either normal or abnormal, and further, benign or malignant. The proposed framework constitutes four computational modules, namely, ROI generation using cropping operation, texture feature extraction using contourlet transformation, a wrapper-based feature selection algorithm, namely, forest optimization algorithm to select the optimal features, and finally different classifiers like SVM, k-NN, Naive Bayes, and C4.5 that are employed to classify the inputs into normal or abnormal, and again benign or malignant. The proposed framework is examined on two widely used standard datasets, namely, MIAS and DDSM. The performance measures are computed with respect to normal vs. abnormal, and benign vs. malignant for four different hybrid CAD models, namely, (Contourlet + FOA + SVM), (Contourlet + FOA + k-NN), (Contourlet + FOA + Naive Bayes), and (Contourlet + FOA + C4.5). The highest classification accuracy of 100% is achieved for normal vs. abnormal classification in case of both MIAS and DDSM. The performance of the proposed hybrid scheme demonstrates its effectiveness with the other state-of-the-art schemes. Experimental results reveal that the proposed hybrid scheme is accurate and robust. Finally, the suggested scheme is considered as a reliable CAD framework to help the physicians for better diagnosis.

Hybrid Rough Set with Black Hole Optimization Based Feature Selection Algorithm for Protein Structure Prediction

Hybrid Rough Set with Black Hole Optimization Based Feature Selection Algorithm for Protein Structure Prediction

Binary whale optimisation: an effective swarm algorithm for feature selection

Feature selection process is considered as one of the most difficult challenges in machine learning and has attracted many researchers recently. The main disadvantages of the classical optimisation algorithms based feature selection are slow convergence speed and local optima stagnation. In this work, a novel binary version of the whale optimisation is proposed for selecting the optimal feature subset and increasing the classification accuracy. The performance of the proposed binary whale optimisation (BWO) is verified by comparisons with three well known optimisation based feature selection algorithms; genetic algorithm, ant colony optimisation and particle swarm optimisation; on nine benchmark datasets. The qualitative and quantitative results show the capability of the proposed BWO to search the feature space for optimal feature combinations. Moreover, results prove that the proposed BWO is able to outperform the current algorithms on the majority of datasets in terms of both average classification accuracy and convergence speed.

Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification

Background and objectiveInfant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. MethodsCry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. ResultsSeveral experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. ConclusionThe experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals.

An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing

Churn prediction in telecom has gained a huge prominence in the recent times due to the extensive interests exhibited by the stakeholders, large number of competitors and huge revenue losses incurred due to churn. Predicting telecom churn is challenging due to the voluminous and sparse nature of the data. This paper presents a technique for the telecom churn prediction that employs particle swarm optimization (PSO) and proposes three variants of PSO for churn prediction namely, PSO incorporated with feature selection as its pre-processing mechanism, PSO embedded with simulated annealing and finally PSO with a combination of both feature selection and simulated annealing. The proposed classifiers were compared with decision tree, naive bayes, K-nearest neighbor, support vector machine, random forest and three hybrid models to analyze their predictability levels and performance aspects. Accuracy, true positive rate, true negative rate, false positive rate, Precision, F-Measures, receiver operating characteristic and precision-recall plots were used as performance metrics. Experiments reveal that the performance of metaheuristics was more efficient and they also exhibited better predictability levels.

Image steganalysis using improved particle swarm optimization based feature selection

Image steganalysis is the task of discovering the hidden message in a multimedia file in which the steganalysis technique highly depends on the feature elements of the image. Since there is a possibility for a feature vector to contain redundant elements, processing of redundant elements can be harmful in terms of long computation cost and large storage space. This paper proposes a new feature selection approach based on Adaptive inertia weight-based Particle Swarm Optimization (APSO) for the image steganalysis where the inertia weight of PSO is adaptively adjusted using the swarm diameter, average distance of particles around the center and average speed of particles towards the center. Also, the proposed APSO is used with the novel measure of Area Under the receiver operating characteristics Curve (AUC) as the fitness function to enhance the performance of identification of stego-images from the cover images in steganalysis problem. Due to appropriate convergence rate and the regulated search step of APSO, it is able to select the most significant and influential feature elements and so, the performance of steganalysis will be improved. Experimental results of the proposed method on Breaking Out Steganography System (BOSS) benchmark proves the superiority of the proposed method compared to the similar approaches in image steganalysis in terms of detection of stego-image, running time, and diversity measure.