Tackling smart city security: deep learning approach utilizing feature selection and two-level cooperative framework optimized by adapted metaheuristics algorithm

Today, diagnosis of attention deficit hyperactivity disorder (ADHD) still primarily relies on a series of subjective evaluations that highly rely on a doctor’s experiences and intuitions from diagnostic interviews and observed behavior measures. An accurate and objective diagnosis of ADHD is still a challenge and leaves much to be desired. Many children and adults are inappropriately labeled with ADHD conditions, whereas many are left undiagnosed and untreated. Recent advances in neuroimaging studies have enabled us to search for both structural (e.g., cortical thickness, brain volume) and functional (functional connectivity) abnormalities that can potentially be used as new biomarkers of ADHD. However, structural and functional characteristics of neuroimaging data, especially magnetic resonance imaging (MRI), usually generate a large number of features. With a limited sample size, traditional machine learning techniques can be problematic to discover the true characteristic features of ADHD due to the significant issues of overfitting, computational burden, and interpretability of the model. There is an urgent need of efficient approaches to identify meaningful discriminative variables from a higher dimensional feature space when sample size is small compared with the number of features. To tackle this problem, this paper proposes a novel integrated feature ranking and selection framework that utilizes normalized brain cortical thickness features extracted from MRI data to discriminate ADHD subjects against healthy controls. The proposed framework combines information theoretic criteria and the least absolute shrinkage and selection operator (Lasso) method into a two-step feature selection process which is capable of selecting a sparse model while preserving the most informative features. The experimental results showed that the proposed framework generated the highest/comparable ADHD prediction accuracy compared with the state-of-the-art feature selection approaches with minimum number of features in the final model. The selected regions of interest in our model were consistent with recent brain–behavior studies of ADHD development, and thus confirmed the validity of the selected features by the proposed approach.

Credit scoring is extensively used by credit industries and financial institutions for financial decision-making. It is a way to assess the risk associated with an applicant based on historical data. However, the historical data may have large number of redundant and noisy features which could affect performance of credit scoring models. Main focus of this paper is to develop a hybrid credit scoring model by combining the feature selection and multi-layer ensemble classifier framework to improve the prediction performance of credit scoring model. The proposed hybrid credit scoring model uses hybrid binary particle swarm optimization and gravitational search algorithm (BPSOGSA) for feature selection and multi-layer ensemble classifier framework with five heterogeneous classifiers. A novel V-shaped transfer function for BPSOGSA is also designed for effective feature selection, which is used to transform the continuous search space to binary search space. Also, a novel fitness function for BPSOGSA is proposed to calculate the fitness value for each search agent. Further, multi-layer ensemble classifier framework along with a novel aggregation function is designed based on generalized convex function. The proposed hybrid credit scoring model is validated using Australian, German-categorical, German-numerical and Japanese credit scoring datasets. The experimental results on all the datasets demonstrate that the proposed credit scoring model outperforms other methods such as random forest and ensemble frameworks, namely majority voting, layered majority voting, weighted voting and layered weighted voting in terms of accuracy, sensitivity, G-measure and ROC characteristics.

Gross domestic product (GDP) is an important index reflecting the economic development of a region. Accurate GDP prediction of developing regions can provide technical support for sustainable urban development and economic policy formulation. In this paper, a novel multi-factor three-step feature selection and deep learning framework are proposed for regional GDP prediction. The core modeling process is mainly composed of the following three steps: In Step I, the feature crossing algorithm is used to deeply excavate hidden feature information of original datasets and fully extract key information. In Step II, BorutaRF and Q-learning algorithms analyze the deep correlation between extracted features and targets from two different perspectives and determine the features with the highest quality. In Step III, selected features are used as the input of TCN (Temporal convolutional network) to build a GDP prediction model and obtain final prediction results. Based on the experimental analysis of three datasets, the following conclusions can be drawn: (1) The proposed three-stage feature selection method effectively improves the prediction accuracy of TCN by more than 10%. (2) The proposed GDP prediction framework proposed in the paper has achieved better forecasting performance than 14 benchmark models. In addition, the MAPE values of the models are lower than 5% in all cases.

Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources.

The action prediction of pedestrians significantly contributes to an intelligent braking system in cars; knowing that the pedestrians will run in several seconds such as for crossing streets, the cars can start braking in advance, to effectively reduce the risk for crash accidents. In this paper, we propose a method to predict how the pedestrian act (run or walk) in the future based on preindication in video frames detected by only appearance-based image features. We empirically mine the distinctive frames that precede the target action, ‘running’ in this case, and are effective for predicting it in the framework of feature selection. By using the most effective frames, we can build the action prediction method by exploiting the image features extracted at those frames. As to the image feature extraction methods, we evaluate two types of features in our method, one is GLAC (Gradient Local AutoCorreration) and the other is HOG (Histogram of Oriented Gradient). In the experiments, the effective frames are successfully found around 0.37 s before running action by using GLAC feature; this is not the case of HOG. We also show that the results are closely related to human motion phases from walking to running via biomechanical analysis.

FANS: a framework for feature selection in sentiment classification using a modified Firefly algorithm

In microarray experiments, the sample size is considerably smaller than that of the feature size, thereby imposing the curse of dimensionality problem. To resolve this issue, evolutionary algorithms are often utilized. In this paper, a novel framework for feature selection and classification of the microarray data is presented. Initially, a statistical filter, namely ANOVA, is used to select the relevant genes (features) from the original set of genes. Then, an evolutionary wrapper-based approach utilizing the principles of enhanced Jaya (EJaya) algorithm and forest optimization algorithm (FOA) is proposed to find the optimal set of genes from the previously selected genes. The main objective of using EJaya is to tune the two important parameters, namely local seeding changes and global seeding changes of FOA. During the selection of the optimal set of genes, support vector machine is employed as a classifier to classify the microarray data. To perform a comprehensive experimental study, the proposed method is tested on both binary-class and multi-class microarray datasets. From the extensive result analysis, it has been observed that the proposed technique achieves better classification accuracy with considerably less number of features than that of the benchmark schemes.

We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex being "discriminative" or "nondiscriminative" for a given classification task. To illustrate the applicability and generality of our framework, we use the estimated probabilities as feature scoring to define compact signatures for three different classification tasks: 1) 3D Face Recognition, 2) 3D Facial Expression Recognition, and 3) Ethnicity-based Subject Retrieval, obtaining very competitive results. The main contribution of this work lies in the development of a novel framework for feature selection in scenaria in which the most discriminative information is smoothly distributed along a lattice.

Feature selection plays an important role in data mining and pattern recognition. However, most existing feature selection methods suffer from stagnation in local optimal and/or high computational cost. In general, the feature selection process can be considered as a global optimization problem. The swarm intelligence algorithm can effectively find the optimal feature subset due to its global search ability. But it usually consumes very long running time when dealing with large data sets. In this paper, feature selection is transformed into a global optimization problem, which provides a fast and efficient method based on swarm intelligence algorithm. First, we propose a global optimization framework for filter-based feature selection and its mathematical model. Furthermore, to solve the feature selection problem for acoustic defect detection, we combine the shuffled frog leaping algorithm (SFLA) with an improved minimum-redundancy maximum-relevancy (ImRMR), named SFLA-ImRMR. In the experiments, a back propagation neural network is employed to evaluate the classification performance of the selected feature subset on the test sets of acoustic defect detection. The results show that SFLA-ImRMR achieved similar performance to the other algorithms within the shortest time.

Many feature selection algorithms have been proposed in the past focusing on improving classification accuracy. In this work, we point out the importance of stable feature selection for knowledge discovery from high-dimensional data, and identify two causes of instability of feature selection algorithms: selection of a minimum subset without redundant features and small sample size. We propose a general framework for stable feature selection which emphasizes both good generalization and stability of feature selection results. The framework identifies dense feature groups based on kernel density estimation and treats features in each dense group as a coherent entity for feature selection. An efficient algorithm DRAGS (Dense Relevant Attribute Group Selector) is developed under this framework. We also introduce a general measure for assessing the stability of feature selection algorithms. Our empirical study based on microarray data verifies that dense feature groups remain stable under random sample hold out, and the DRAGS algorithm is effective in identifying a set of feature groups which exhibit both high classification accuracy and stability.

In this work we propose a novel, sound framework for evolutionary feature selection in unsupervised machine learning problems. We show that unsupervised feature selection is inherently multi-objective and behaves differently from supervised feature selection in that the number of features must be maximized instead of being minimized. Although this might sound surprising from a supervised learning point of view, we exemplify this relationship on the problem of data clustering and show that existing approaches do not pose the optimization problem in an appropriate way. Another important consequence of this paradigm change is a method which segments the Pareto sets produced by our approach. Inspecting only prototypical points from these segments drastically reduces the amount of work for selecting a final solution. We compare our methods against existing approaches on eight data sets.

The current study considers an unconventional framework of unsupervised feature selection for supervised learning. We provide a new unsupervised algorithm which we call LIA, for Label-Independent Algorithm, which combines information theory and network science techniques. In addition, we present the results of an empirical study comparing LIA with a standard supervised algorithm (MRMR). The two algorithms are similar in that both minimize redundancy, but MRMR uses the labels of the instances in the input data set to maximize the relevance of the selected features. This is an advantage when such labels are available, but a shortcoming when they are not. We used cross-validation to evaluate the effectiveness of selected features for generating different well-known classifiers for a variety of publicly available data sets. The results show that the performance of classifiers using the features selected by our proposed label-independent algorithm is very close to or in some cases better than their performance when using the features selected by a common label-dependent algorithm (MRMR). Thus, LIA has potential to be useful for a wide variety of applications where dimension reduction is needed and the instances in the data set are unlabeled.

An efficient framework for unsupervised feature selection

Feature selection is an important preprocessing in data mining, it aims to reduce the computational complexity of learning algorithm, and to improve the performance of data mining algorithms by removing irrelevant and redundant features. In the framework of discrete-valued feature selection, this paper experimentally compares two feature selection methods which are based on generic algorithm. The former uses relative classification information entropy to measure the significance of the candidate feature subsets, while the later uses inconsistency ratio to evaluate feature subsets. Both methods use genetic algorithm to search the optimal feature subset, we find by experimental comparisons that the former outperforms the latter.

Unsupervised feature selection has shown significant potential in distance-based clustering tasks. This paper proposes a novel triplet induced method. Firstly, a triplet-based loss function is introduced to enforce the selected feature groups to preserve ordinal locality of original data, which contributes to distance-based clustering tasks. Secondly, we simplify the orthogonal basis clustering by imposing an orthogonal constraint on the feature projection matrix. Consequently, a general framework for simultaneous feature selection and clustering is discussed. Thirdly, an alternating minimization algorithm is employed to efficiently optimize the proposed model together with rapid convergence. Extensive comparison experiments on several benchmark datasets well validate the encouraging gain in clustering from our proposed method.