Design and Procedures for the Investigation Conducted

This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, as search strategies, and the generalized F-score (GF) to evaluate the importance of each feature. The new accuracy measure is used as the criterion to evaluated the performance of a temporary SVM to direct the feature selection algorithms. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the stable and efficient classifiers. To get the stable, statistical and optimal classifiers, we conduct 10-fold cross validation experiments in the first stage; then we merge the 10 selected feature subsets of the 10-cross validation experiments, respectively, as the new full feature set to do feature selection in the second stage for each algorithm. We repeat the each hybrid feature selection algorithm in the second stage on the one fold that has got the best result in the first stage. Experimental results show that our proposed two-stage hybrid feature selection algorithms can construct efficient diagnostic models which have got better accuracy than that built by the corresponding hybrid feature selection algorithms without the second stage feature selection procedures. Furthermore our methods have got better classification accuracy when compared with the available algorithms for diagnosing erythemato-squamous diseases.

Partial multi-label feature selection based on label disambiguation and double-regularized sparse regression

The immune system is a complicated defensive system that comprises multiple functional cells and molecules acting against endogenous and exogenous pathogenic factors. Identifying immune cell subtypes and recognizing their unique immunological functions are difficult because of the complicated cellular components and immunological functions of the immune system. With the development of transcriptomics and high‐throughput sequencing, the gene expression profiling of immune cells can provide a new strategy to explore the immune cell subtyping. On the basis of the new profiling data of mouse immune cell gene expression from the Immunological Genome Project (ImmGen), a novel computational pipeline was applied to identify different immune cell subtypes, including αβ T cells, B cells, γδ T cells, and innate lymphocytes. First, the profiling data was analyzed by a powerful feature selection method, Monte‐Carlo Feature Selection, resulting in a feature list and some informative features. For the list, the two‐stage incremental feature selection method, incorporating random forest as the classification algorithm, was applied to extract essential gene signatures and build an efficient classifier. On the other hand, a rule learning scheme was applied on the informative features to construct quantitative expression rules. A group of gene signatures was found as qualitatively related to the biological processes of four immune cell subtypes. The quantitative expression rules can efficiently cluster immune cells. This work provides a novel computational tool for immune cell quantitative subtyping and biomarker recognition.

With the increasing penetration of renewable energy in China’s power system, wide-band oscillations with multiple modes have emerged, posing new challenges to the assessment of renewable energy oscillation hosting capacity. At present, the construction of artificial intelligence-based assessment models still relies heavily on researchers’ subjective experience when selecting input features, which lacks theoretical justification. Moreover, the expansion of system scale increases data dimensionality and introduces a higher risk of model overfitting. To address these issues, this paper proposes a two-stage key feature selection method based on participation factors and XGBoost. First, the participation factor theory is utilized to establish the functional mapping between system electrical quantities and oscillatory characteristics, enabling an initial identification of the electrical variables most relevant to renewable energy oscillation hosting capacity. Second, to mitigate the curse of dimensionality brought by large-scale systems, a variational autoencoder is employed to compress the initial feature set and extract its latent representations. Finally, XGBoost is applied to these latent representations to further identify the most critical features that accurately reflect the oscillation hosting capacity of renewable energy. Experimental results on a wide-band oscillation dataset show that active power achieves the highest importance score among all features; moreover, a model using only active-power data attains an accuracy of approximately 97%, demonstrating its effectiveness as the most strongly correlated and least redundant key feature subset.

Diagnosis of tapping process using spindle motor current

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and develop drugs. However, the high computational cost due to the “curse of dimensionality” and the high redundancy among features limit the application of evolutionary algorithms to the feature selection problem for high-dimensional data. This paper proposes a two-stage hybrid feature selection method to address this problem. In the first stage, a simple and efficient filtering me thod is used to initially filter redundant features, reduce the feature dimensionality, and reduce the search space of the evolutionary algorithm in the second stage. In the second stage, we propose an improved differential evolution algorithm. We redesign the binary quantization of the differential evolution algorithm for the characteristics of microarray data and improve the algorithm’s variation process to balance the algorithm’s search efficiency and accuracy. In addition, we define the redundancy of feature subsets and add it to the fitness function to reduce the redundancy of the final feature subsets. The proposed method is compared with classical feature selection methods and advanced hybrid feature selection methods on eight publicly available microarray data, and the effectiveness and advancement of the proposed method are demonstrated.

Big data is technology that has the ability to manage very large amounts of data, in very fast time to allow real-time analysis and reactions. Several clustering methods which are used to group data are Fuzzy C-Means (FCM) and K-Means Clustering. K-Means Clustering algorithm is a method of partitioning existing data into two or more group. This research goal was to compare the performance of K-Means and Fuzzy C-Means algorithms in clustering data using big data technology. In this research, Hadoop and Hive were chosen the big data technology. The knowledge of Shia history on student and lecturer of Syarif Hidayatullah State Islamic University Jakarta were the data which used in this research, The testing was done by constructing application K-Means Fuzzy C-Means using Java language, Hadoop and Hive and then test the performance of K-Means and Fuzzy C-Means algorithms in data clustering. It compares both algorithms in terms of accuracy, execution time, and time complexity of the algorithms. In the application K-Means Fuzzy C-Means, evaluation were performed with data filter and the average accuracy difference result of K-Means and Fuzzy C-Means is 8.03% with the better accuracy owned by K-Means. The average execution time difference is 718.58 ms, which K-Means was faster than is Fuzzy C-Means. The time complexities of both algorithms have the same value O(n2) and the Big O equation resulted in an average difference of 93,568 with the smallest value on K-Means. Thus, K-Means algorithm is better than the Fuzzy C-Means in terms of accuracy, execution time, and the time complexity.

Advancements in technology make the application domains to collect large amounts of data that is used for future decision making by applying various techniques such as classification, regression, etc. These techniques comprise machine learning algorithms. The running time complexity of these machine learning algorithms is determined by the number of instances and the total of all features or dimensions in the dataset. All the instances are generally required. But all the features in the dataset may not be related or useful in the machine learning task, for example, in classification. In such a case, the dimensionality of the dataset is reduced to optimize the running time complexity of the algorithms without compromising the accuracy. Reducing the dimensionality is achieved by extracting only those features which are more relevant. Hence feature extraction or dimensionality reduction plays a significant role in obtaining high accuracy of machine learning algorithms within the acceptable time complexity. The existing techniques for feature extraction are principal component analysis and by using correlation coefficients. But these techniques required more time complexity and some sort of manual decisions for selecting optimal features. To extract the most optimal features, a novel algorithm is proposed using an evolutionary optimization technique called particle swarm optimization (PSO). As a fitness function in PSO, an artificial neural network classifier pertaining to its efficient performance in classification tasks. The performance of the proposed algorithm is verified by five benchmark datasets from the UCI repository. The model is evaluated with different performance measures:accuracy, precision, recall, and the F-1 score. Research results have proven that algorithm proposed here reduces the number of features by improving the performance of the machine learning sub-system when compared to conventional algorithms.

Feature selection aims at selecting a feature subset that has the most discriminative information and preserve most of characteristics from original features in HyperSpectral Image (HSI) classification. This paper proposes a two-stage feature selection method based on Mutual Information (MI) and Je ffries-Matusita (J-M) measure. In first stage, we select a feature subset with minimal redundancy maximal relevance criteria. In second stage, we select further a feature subset from which obtained in first stage by maximizing J-M distance. Multiple Kernel Learning (MKL) and Ensemble Learning (EL) are promising family of machine learning algorithms and have been applied extensively in HSI classification. Many MKL methods often formulate the problem as an optimization task. To avoid solving the complicated optimization problem, this paper presents an ensemble learning framework, SMKB (Stochastic Multiple Kernel Boosting), which applies Adaptive Boosting (AdaBoost) and stochastic approach to learning multiple kernel-based classifier for multi-class classification problem. We examine empirical performance of proposed approach on benchmark hyperspectral classification data set in comparison with various state-of-the-art algorithms. Experimental results demonstrate that the proposed method obtains better feature subsets and is more effective and efficient than classical methods.

Air pollution levels have remained a significant issue worldwide despite advancements in technology, primarily due to rapid industrialization and urbanization. Among the various pollutants, PM2.5 significantly impacts air quality, posing health risks such as respiratory and cardiovascular diseases. Accurate prediction of PM2.5 levels is essential for effective air quality management. However, multicollinearity in air quality data can hinder model performance. To address this issue, this study introduces the AdjcorT-RBFNN, a two-stage feature selection method, to classify air quality in Klang, Selangor. The AdjcorT-RBFNN model selects the optimal combination of 9 feature combinations from 10 variables and outperforms the RBFNN model, which uses all 10 variables. With 7 hidden nodes and a learning rate of 0.01 for both models, AdjcorT-RBFNN achieves higher accuracy (0.62), sensitivity (0.64), specificity (0.60), precision (0.60), F1 score (0.62), and AUROC (0.62), confirming its effectiveness in classification tasks. The optimal features for predicting air quality in Klang are identified as PM2.5, PM10, relative humidity, SO2, wind direction, O3, CO, ambient temperature, and NO2. Monte Carlo simulations validate the model’s effectiveness, showing that AdjcorT-RBFNN consistently outperforms RBFNN, especially with strong negative correlations (ρ=-0.8) and larger sample sizes (N=150 and 200) further enhance classification accuracy. Compared to RBFNN, AdjcorT-RBFNN enhances class discrimination and reduces false positives, improving its reliability in detecting true classifications. These findings highlight the importance of feature selection in improving model performance, particularly in datasets with multicollinearity. Researchers, and health organizations can leverage AdjcorT-RBFNN for more accurate air quality predictions, supporting informed pollution control strategies.

This paper presents a wavelet-based texture analysis method for classification of melanoma. The method applies tree-structured wavelet transform on different color channels of red, green, blue and luminance of dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. Feature extraction and a two-stage feature selection method, based on entropy and correlation, were applied to a train set of 103 images. The resultant feature subsets were then fed into four different classifiers: support vector machine, random forest, logistic model tree and hidden naive bayes to classify melanoma in a test set of 102 images, which resulted in an accuracy of 88.24% and ROC area of 0.918. Comparative study carried out in this paper shows that the proposed feature extraction method outperforms three other wavelet-based approaches.

Selecting distinguishable features from small sample data of rolling bearings has become an emerging and trending research topic in recent years. In this paper, a two-stage feature selection method based on Euclidean distance and multi-class support vector machine is proposed to remove irrelevant features and redundant features. First, the fault data are formed to a multidomain feature set. Second, the ReliefF algorithm based on Euclidean distance is used to remove irrelevant features. Third, the support vector machine recursive feature elimination (SVMRFE) is used to remove redundant features. Lastly, the KNN classifier is used to verify the effectiveness of this method. This paper uses the rolling bearing public datasets. After comparison, the results prove that the method proposed in the article is better.

Most of works on the time complexity analysis of evolutionary algorithms have always focused on some artificial binary problems. The time complexity of the algorithms for combinatorial optimisation has not been well understood. This paper considers the time complexity of an evolutionary algorithm for a classical combinatorial optimisation problem, to find the maximum cardinality matching in a graph. It is shown that the evolutionary algorithm can produce a matching with nearly maximum cardinality in average polynomial time.

In this paper, we focus on batch state estimation for linear systems. This problem is important in applications such as environmental field estimation, robotic navigation, and target tracking. Its difficulty lies on that limited operational resources among the sensors, e.g., shared communication bandwidth or battery power, constrain the number of sensors that can be active at each measurement step. As a result, sensor scheduling algorithms must be employed. Notwithstanding, current sensor scheduling algorithms for batch state estimation scale poorly with the system size and the time horizon. In addition, current sensor scheduling algorithms for Kalman filtering, although they scale better, provide no performance guarantees or approximation bounds for the minimization of the batch state estimation error. In this paper, one of our main contributions is to provide an algorithm that enjoys both the estimation accuracy of the batch state scheduling algorithms and the low time complexity of the Kalman filtering scheduling algorithms. In particular: 1) our algorithm is near-optimal: it achieves a solution up to a multiplicative factor 1/2 from the optimal solution, and this factor is close to the best approximation factor 1/e one can achieve in polynomial time for this problem; 2) our algorithm has (polynomial) time complexity that is not only lower than that of the current algorithms for batch state estimation; it is also lower than, or similar to, that of the current algorithms for Kalman filtering. We achieve these results by proving two properties for our batch state estimation error metric, which quantifies the square error of the minimum variance linear estimator of the batch state vector: a) it is supermodular in the choice of the sensors; b) it has a sparsity pattern (it involves matrices that are block tri-diagonal) that facilitates its evaluation at each sensor set.

The authors analyze the parallel time-complexity of a heuristic algorithm for solving the NP-hard k-center problem with usage weights. The problem is to choose k vertices as service centers so that the maximum weighted service delivery distance to any vertex is minimized. The algorithm uses a greedy strategy to choose the vertex with maximum usage weights as the next service vertex. It generates results that are guaranteed to be no greater than twice the optimal solution values, which is the best possible polynomial time heuristic unless P=NP, and its uniprocessor time complexity is O(n/sup 3/). The authors show that the algorithm may be implemented on a multiprocessor system in O(n log/sup 2/ n) time, where n is the total number of vertices. They further show that even with unlimited parallelism, this algorithm will have higher than polylogarithmic time complexity unless polylogspace=P.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>