Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation

The exponential growth of data in the information system has led the concept of big data. High dimensional data imposes serious challenges to the useful information. It limits the traditional machine learning algorithms and techniques to preprocess, learn, and analyze the data. The optimal subset selection is an effective solution. Thus, feature and instance selection are used for data reduction. In this paper, we have chosen instance subset selection to increase the accuracy of data analysis. The study is to propose an approach to select the optimal set of instances using Genetic Algorithm and is also aimed to optimize the Random Forest using GA for the boosted accuracy in the classification. The study introduced an approach that uses Genetic Algorithm to select the optimal instance subset. GA is also implemented with the Random Forest algorithm to select the best parameters that are stored in the DEAP library for the accurate classification. The experimentation results show that Genetic Algorithm effectively selects the best instance subset, and the combination of Random Forest and Genetic Algorithm outperforms with 85.8%. This method not only shows how evolutionary techniques can be used for data-driven optimization, but it also makes the model more efficient by decreasing data redundancy. The findings provide credence to the idea that combining Genetic Algorithms with more conventional machine learning techniques can improve performance.

One of the important practical applications of computer vision techniques is computer-aided diagnosis (CAD) in medical imaging. It has been shown that CAD can improve the accuracy of breast cancer detection and characterization by radiologists on mammograms. In this chapter, we discuss an important step — feature selection — in classifier design for CAD algorithms. Feature selection reduces the dimensionality of an available feature space and is therefore often used to prevent over-parameterization of a classifier. Many feature selection techniques have been proposed in the literature. We will illustrate the usefulness of genetic algorithms (GAs) for feature selection by comparing GA with a commonly used sequential selection method. A brief introduction to the GA is given and several examples using GA feature selection for the characterization of mammographic lesions are discussed. The examples illustrate the design of a fitness function for optimizing classification accuracy in terms of the receiver operating characteristics of the classifier, the dependence of GA performance on its evolution parameters, and the design of a fitness function tailored to a specific classification task.

Using image classification approach for automatic image annotation is one promising method. In order to improve image annotation accuracy, recent researchers propose to use AdaBoost algorithm for the ensemble of classifiers. But in these researches, only fewer features are used. We construct multi-class classifiers for all the image low-level feature of multimedia content description interface and their potential combinations respectively, k-nearest neighbor classifier is used as the base classifier and dasiaone vs. onepsila scheme is chosen to build multi-class classifiers. A bi-coded chromosome genetic algorithm is used to select the optimal classifier subset as weak classifiers and corresponding weights, which are used for the combination of an ensemble classifier by weighted majority voting scheme. The results of experiment over 2000 classified Corel images show that the approach selects 4 of 325 multi-class classifiers as weak classifiers as well as corresponding optimized weights to generate an ensemble classifier. The ensemble classifier created by the bi-coded chromosome genetic algorithm has higher accuracy than that by AdaBoost algorithm.

The goal of the feature selection process is, given a dataset described by n attributes (features), to find the minimum number m of relevant attributes which describe the data as well as the original set of attributes do. Genetic algorithms have been used to implement feature selection algorithms. Previous algorithms presented in the literature used the predictive accuracy of a specific learning algorithm as the fitness function to maximize over the space of possible feature subsets. Such an approach to feature selection requires a large amount of CPU time to reach a good solution on large datasets. This paper presents a genetic algorithm for feature selection which improves previous results presented in the literature for genetic-based feature selection. It is independent of a specific learning algorithm and requires less CPU time to reach a relevant subset of features. Reported experiments show that the proposed algorithm is at least ten times faster than a standard genetic algorithm for feature selection without a loss of predictive accuracy when a learning algorithm is applied to reduced data.

In tissue diagnosis with microarray data, selecting the optimal gene subset is very essential for various merits. Different from existing methods, we propose a novel optimal gene subset selection method based on genetic algorithms (GAs). Special fitness function is applied in this scheme. This GA-based method automatically determines the size of a predictive gene group, as well as the members in such a group. The evaluation experiments are applied to two popular data sets. The results and some discussions are presented too.

A belt conveyor roller defect detection method based on genetic algorithm feature selection and stacking learning was proposed. Roller defect data from 1620 samples of three types of roller defects was first collected. To improve the detection efficiency of roller defects, a feature selection method based on a genetic algorithm for selecting roller defect features was proposed. The model classification efficiency of roller features after feature selection by the genetic algorithm was found to be higher. The feature subset with the highest fitness had a feature number of 35. A stacking learning model for detecting roller defects was also constructed. The stacking method enhanced the comprehensive performance of the base learner model for detecting roller defects. The stacking learning model finally achieved a classification accuracy of 94.86% on the roller defect dataset. The classification performance of the stacking learning model was better than that of its base learning model. The results showed that the stacking learning model was preferred for detecting roller defects. The proposed conveyor belt roller defect detection method, while achieving high accuracy, may be limited by potential overfitting and sensitivity to environmental conditions. The next steps in refining the conveyor belt roller defect detection method will focus on dataset expansion, algorithm parameter tuning, and environmental robustness testing.

Class-Specific feature selection for enhanced cattle behavior recognition using wearable-based system

Quality based classification of gasoline samples by ATR-FTIR spectrometry using spectral feature selection with quadratic discriminant analysis

Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the time, frequency or time-frequency domains. To explore the salient information provided by the acoustic emission (AE) signals, a hybrid of feature pool creation and an optimal features subset selection mechanism is proposed for crack detection in a spherical tank. The optimal hybrid feature pool creation process is composed of two major parts: (1) extraction of statistical features from time and frequency domains, as well as extraction of traditional features associated with the AE signals; and (2) genetic algorithm (GA)-based optimal features subset selection. The optimal features subset is then provided to the k-nearest neighbor (k-NN) classifier to distinguish between normal (NC) and crack conditions (CC). Experimental results show that the proposed approach yields an average 99.8% accuracy for heath state classification. To validate the effectiveness of the proposed approach, it is compared to conventional non-linear dimensionality reduction techniques, as well as those without feature selection schemes. Experimental results show that the proposed approach outperforms conventional non-linear dimensionality reduction techniques, achieving at least 2.55% higher classification accuracy.

We investigate the usefulness of evolutionary algorithms in three incarnations of the problem of feature relevance assignment in memory-based language processing (MBLP): feature weighting, feature ordering and feature selection. We use a simple genetic algorithm (GA) for this problem on two typical tasks in natural language processing: morphological synthesis and unknown word tagging. We find that GA feature selection always significantly outperforms the MBLP variant without selection and that feature ordering and weighting with GA significantly outperforms a situation where no weighting is used. However, GA selection does not significantly do better than simple iterative feature selection methods, and GA weighting and ordering reach only similar performance as current information-theoretic feature weighting methods.

An approach combining optimal antenna subset selection with blind detection scheme for Orthogonal Space-Time Block Coding (OSTBC) is proposed in this paper. The optimal antenna subset selection is taken into account at transmitter and/or receiver sides, which chooses the optimal antennas to increase the diversity order of OSTBC and improve further its performance. In order to enhance the robustness of the detection used in the conventional OSTBC scheme, a blind detection scheme based on Independent Component Analysis (ICA) is exploited which can directly extract transmitted signals without channel estimation. Performance analysis shows that the proposed approach can achieve the full diversity and the flexibility of system design by using the antenna selection and the ICA based blind detection schemes.

A feature selection approach combining genetic algorithm (GA) with mutual information (FSGM) is proposed. In fact, FSGM is a genetic algorithm applied to feature selection. For feature selection task, an individual of GA represents a feature subset, and the fitness function is the evaluation of the feature subset. With elaborating design, the global searching and completely evaluation can be realized in FSGM. The experimental results confirm the effectiveness of the proposed algorithm in improving the generalization and reducing the over fitting of selected feature subset.

Electrical energy consumption continues to increase every year, so accurate prediction models are needed to support household electrical energy efficiency. This study analyzed high-resolution household electricity consumption data using the Random Forest (RF) algorithm and evaluated the influence of feature selection based on Genetic Algorithms (GA) in improving the performance of RF predictions. The base RF model achieves an RMSE of 0.6148, a MAE of 0.3478, and an R² of 0.5047. After implementing GA-based feature selection, the RF model with GA yields an RMSE of 0.6125 and an R² of 0.5084, indicating a marginal performance improvement. However, the MAE value increased slightly to 0.3503, which suggests that the increase was not uniform across the evaluation metrics. Overall, the RF approach with GA offers a modest improvement in prediction stability but with very limited accuracy gains, which highlights its potential and limitations in optimizing household energy consumption prediction.

In recent years, detecting credit card fraud transactions has been a difficult task due to the high dimensions and imbalanced datasets. Selecting a subset of important features from a high-dimensional dataset has proven to be the most prominent approach for solving high-dimensional dataset issues, and the selection of features is critical for improving classification performance, such as the fraud transaction identification process. To contribute to the field, this paper proposes a novel feature selection (FS) approach based on a metaheuristic algorithm called Rock Hyrax Swarm Optimization Feature Selection (RHSOFS), inspired by the actions of rock hyrax swarms in nature, and implements supervised machine learning techniques to improve credit card fraud transaction identification approaches. This approach is used to select a subset of optimal relevant features from a high-dimensional dataset. In a comparative efficiency analysis, RHSOFS is compared with Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS) in a comparative efficiency analysis. The proposed RHSOFS outperforms existing approaches, such as DEFS, GAFS, PSOFS, and ACOFS, according to the experimental results. Various statistical tests have been used to validate the statistical significance of the proposed model.

Rapid and accurate ambiguity resolution is the core of high-precision precise point positioning (PPP) data processing. However, the ambiguity parameters in PPP observation models are easily affected by atmospheric residual and gross errors, which lead to the probability of successfully fixing decreases and computational burden increases in full ambiguity resolution. Therefore, an increasing number of partial ambiguity resolution (PAR) strategies have been proposed. The selection of the optimal subset of PAR is crucial in this method. The traditional optimal subset selection method of PAR commonly leads to a single judgment criterion and weakened geometric configuration strength because the satellites with low elevation angles are often easily eliminated during the optimal subset selection. In this paper, a multi-factor constrained optimal subset selection method for PAR was proposed, which incorporates the ambiguity variance, the ambiguity dilution of precision (ADOP), satellite position dilution of precision (PDOP) and ratio test values. In order to verify the feasibility of the proposed optimal subset selection method, PAR tests under two schemes were performed for GPS/Galileo based on the static observation data of 15 Multi-GNSS Experiment (MGEX) tracking stations. The results show that, compared with the ambiguity variance sorting method, the proposed subset selection method can further improve the accuracy of the coordinate solution and the strength of geometric figure positioning. The average root mean square of the coordinate residuals is found to decrease by about 12.90%, 6.83% and 9.39% in the eastern, northern and vertical directions, respectively. The increase in the fixed epoch rate ranged from 0.87% to 33.33%, with an average of about 8.71%.