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In this paper, some significant efforts on fuzzy oblique decision tree (FODT) have been done to improve classification accuracy and decrease tree size. Firstly, to eliminate data redundancy and improve classification efficiency, a forward greedy fast feature selection algorithm based on neighborhood rough set (NRS_FS_FAST) is introduced. Then, a new fuzzy rule generation algorithm (FRGA) is proposed to generate fuzzy rules. These fuzzy rules are used to construct leaf nodes for each class in each layer of the FODT. Different from the traditional axis-parallel decision trees and oblique decision trees, the FODT takes dynamic mining fuzzy rules as decision functions. Moreover, the parameter δ, which can control the size of the tree, is optimized by genetic algorithm. Finally, a series of comparative experiments are carried out with five traditional decision trees (C4.5, Best First Tree (BFT), amulti-class alternating decision tree (LAD), Simple Cart (SC), Naive Bayes Tree (NBT)), and recently proposed decision trees (FRDT, HHCART, and FMMDT-HB) on UCI machine learning datasets. The experimental results demonstrate that the FODT exhibits better performance on classification accuracy and tree size than the chosen benchmarks. [ABSTRACT FROM AUTHOR]

Multimedia data is crucial in the military, medical, forensics, social, etc., to transmit a large amount of data. Security of this sensitive information is the primary issue. This paper uses Latin square and machine learning techniques such as neural networks and genetic algorithm to design an image encryption algorithm. A new neural network-based pseudorandom number generator is proposed to generate a chaotic sequence for various applications. Encryption key images are designed using Latin squares in the finite field. Further, the Latin squares are XOR with the input matrix to get the encrypted images. The proposed algorithm is iterated a finite number of times to generate a cipher image population. Randomly two parents are chosen from the generated population, and row and column arrangements produce offspring. A genetic algorithm is the optimization technique used for the best encrypted image search. The pixel correlation value serves as a fitness function. Finally, the least correlated cipher image is obtained from the genetic algorithm applied to the parent and offspring of the population generated from the encryption algorithm. The simulation results from the proposed image encryption model surpass many communication channel attacks and perform better when compared to existing image security algorithms. [ABSTRACT FROM AUTHOR]

Heart disease is the most life-threatening disease globally, affecting human life very critically. On-time and precise diagnosis of heart disease is vital for the prevention and treatment of heart failure. In the earlier stage, several detection techniques were developed to detect heart disease using machine learning techniques. But the heart problems have not been detected accurately with the minimum required time. This paper presented an effective heart disease prediction system that accurately diagnoses abnormality within a short duration. The feature selection process is carried with stochastic gradient boosting with a recursive feature elimination approach. For classification, the features are clustered by using the adaptive Harris hawk optimization clustering approach. Based on clustered features, the classification is processed with an enhanced deep genetic algorithm. This method improves the performance of the deep neural network by augmenting its initial weights with an enhanced genetic algorithm, which recommends better weights for the neural network. The demonstration of the proposed work is simulated in the MATLAB platform and the dataset used in UCI machine learning repository. The experimental outcome shows that the developed feature selection and classification approach attain 98.36% and 97.3% accuracy compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Feature selection is one of the hottest machine learning topics in recent years. The main purposes of it are to simplify the original model, improve the readability of the model, and prevent over-fitting by searching for a suitable subset of features. There are many methods for this problem, including evolutionary algorithms and particle swarm optimization. Among them, the competitive swarm optimizer is a new optimization algorithm proposed in recent years, which is based on particle swarm optimization algorithm, and has achieved good results in high-dimensional feature selection problems, but it also has the problems of high computation time cost and easily being premature. Aiming at these problems, this paper proposes to add the crossover operator and mutation operator in the genetic algorithm to the competitive swarm optimization, so as to improve the generation speed of new individuals in the algorithm and prevent premature population. After testing on UC Irvine Machine Learning Repository, the new algorithm not only improves the computational efficiency, but also avoids the problem that the competitive swarm optimization algorithm is easy to fall into the local optimum, which greatly improves the calculation effect. [ABSTRACT FROM AUTHOR]

Recognition of moving objects in video images is mainly based on acquiring the target information in a certain time series. After image processing, relevant algorithms are used to get the internal features and effectively identify the target object. However, image background, noise, definition and other factors will have impacts on mobile object recognition. Therefore, the mobile objects in video images are more complicated than the static objects in the fixed images. The traditional convolutional neural network (CNN) uses gradient descent algorithm for learning and training, and uses gradient descent algorithm to determine the initial thresholds, weights, which may cause the training to fall into a local optimal state. Therefore, this paper proposes an improved adaptive genetic algorithm combined with CNN. The thresholds and weights of CNN can be optimized by using adaptive genetic algorithm (AGA), which can overcome the shortcomings of the original genetic algorithm such as slow convergence. Experimental results shows that the recognition accuracy rate of the experiment increased from 83.75% to 92%, the method can effectively improve the accuracy and efficiency of mobile object recognition. [ABSTRACT FROM AUTHOR]

Job shop scheduling problem is a well-known NP problem. It is limited by various conditions. As the scale of the problem increases, the difficulty of finding the optimal solution will increase. It is a difficult combination problem. Limited by the constraints of the actual production environment, how to effectively arrange the processing order of each part will directly affect the production efficiency, the appropriate production scheduling algorithm can correctly and effectively plan the enterprise resources and rationally arrange the processing order and processing time of the workpiece. Proper use of existing resources, by optimizing production scheduling instructions, to meet the basic requirements of production scheduling, in order to obtain the optimization of total production time, has important theoretical significance for the actual production of enterprises. In this paper, the mathematical model is abstracted on the basis of the production scheduling problem. According to the different parts of the same machine and the different processes of the same part, the corresponding processing time and waiting time are obtained. At the same time, the genetic algorithm is improved by genetic algorithm. A dynamic genetic operator based on the number of iterations is proposed, which further enhances the convergence performance and search ability of the genetic algorithm. Through the simulation of MATLAB simulation program, combined with the scheduling standard example, the performance analysis of different algorithms is carried out, the search efficiency of genetic algorithm is improved, the convergence performance of the algorithm is improved, and different optimization choices are obtained for different time weights. The operation results of the system meet the requirements of production scheduling, which proves the feasibility and practicability of the improved genetic algorithm. [ABSTRACT FROM AUTHOR]

Imbalanced problems are quite pervasive in many real-world applications. In imbalanced distributions, a class or some classes of data, called minority class(es), is/are under-represented compared to other classes. This skewness in the data underlying distribution causes many difficulties for typical machine learning algorithms. The notion becomes even more complicated when machine learning algorithms are to combat multi-class imbalanced problems. The presented solutions for tackling the issues arising from imbalanced distributions, generally fall into two main categories: data-oriented methods and model-based algorithms. Focusing on the latter, this paper suggests an elegant blend of boosting and over-sampling paradigms, which is called MDOBoost, to bring considerable benefits to the learning ability of multi-class imbalanced data sets. The over-sampling technique introduced and adopted in this paper, Mahalanobis distance-based over-sampling technique (MDO in short), is delicately incorporated into boosting algorithm. In fact, the minority classes are over-sampled via MDO technique in such a way that they almost preserve the original minority class characteristics. MDO, in comparison with the popular method in this field, SMOTE, generates more similar minority class examples to original class samples. Moreover, the broader representation of minority class examples is provided via MDO, and this, in turn, causes the classifier to build larger decision regions. MDOBoost increases the generalization ability of a classifier, since it indicates better results with pruned version of C4.5 classifier; unlike other over-sampling/boosting procedures, which have difficulties with pruned version of C4.5. MDOBoost is applied to real-world multi-class imbalanced benchmarks and its performance is then compared with several data-level and model-based algorithms. The empirical results and theoretical analyses reveal that MDOBoost offers superior advantages compared to popular class decomposition and over-sampling techniques in terms of MAUC, G-mean, and minority class recall. [ABSTRACT FROM AUTHOR]

Time-split cross-validation is broadly recognized as the gold standard for validating predictive models intended for use in medicinal chemistry projects. Unfortunately this type of data is not broadly available outside of large pharmaceutical research organizations. Here we introduce the SIMPD (simulated medicinal chemistry project data) algorithm to split public data sets into training and test sets that mimic the differences observed in real-world medicinal chemistry project data sets. SIMPD uses a multi-objective genetic algorithm with objectives derived from an extensive analysis of the differences between early and late compounds in more than 130 lead-optimization projects run within the Novartis Institutes for BioMedical Research. Applying SIMPD to the real-world data sets produced training/test splits which more accurately reflect the differences in properties and machine-learning performance observed for temporal splits than other standard approaches like random or neighbor splits. We applied the SIMPD algorithm to bioactivity data extracted from ChEMBL and created 99 public data sets which can be used for validating machine-learning models intended for use in the setting of a medicinal chemistry project. The SIMPD code and simulated data sets are available under open-source/open-data licenses at github.com/rinikerlab/molecular_time_series. [ABSTRACT FROM AUTHOR]

The paper presents an algorithm for integrated scheduling, dispatching, and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm. The algorithm generates an integrated schedule and detail routing paths while optimizing makespan, AGV travel time, and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The multi-objective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance improvements of the last two previous generations. Computational experiments was conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. Integrated scheduling of jobs in FMS which are in synchrony with AGV dispatching, scheduling, and routing proved to ensure the feasibility and effectiveness of all the solutions of the integrated constituent elements. [ABSTRACT FROM AUTHOR]

Instance selection is an important research problem of data pre-processing in the data mining field. The aim of instance selection is to reduce the data size by filtering out noisy data, which may degrade the mining performance, from a given dataset. Genetic algorithms have presented an effective instance selection approach to improve the performance of data mining algorithms. However, current approaches only pursue the simplest evolutionary process based on the most reasonable and simplest rules. In this paper, we introduce a novel instance selection algorithm, namely a genetic-based biological algorithm (GBA). GBA fits a 'biological evolution' into the evolutionary process, where the most streamlined process also complies with the reasonable rules. In other words, after long-term evolution, organisms find the most efficient way to allocate resources and evolve. Consequently, we can closely simulate the natural evolution of an algorithm, such that the algorithm will be both efficient and effective. Our experiments are based on comparing GBA with five state-of-the-art algorithms over 50 different domain datasets from the UCI Machine Learning Repository. The experimental results demonstrate that GBA outperforms these baselines, providing the lowest classification error rate and the least storage requirement. Moreover, GBA is very computational efficient, which only requires slightly larger computational cost than GA. [ABSTRACT FROM AUTHOR]

In this manuscript, we identify and evaluate some of the most used optimization models for rule extraction using genetic programming-based algorithms. Six different models, which combine the most common fitness functions, were tested. These functions employ well-known metrics such as support, confidence, sensitivity, specificity, and accuracy. The models were then applied in the assessment of the performance of a single algorithm in several real classification problems. Results were compared using two different criteria: accuracy and sensitivity/specificity. This comparison, which was supported by statistical analysis, pointed out that the use of the product of sensitivity and specificity provides a more realistic estimation of classifier performance. It was also shown that the accuracy metric can make the classifier biased, especially in unbalanced databases. [ABSTRACT FROM AUTHOR]

Optimization problems aim to minimize or maximize an objective function while fulfilling related constraints. This objective function may be a single or multi-objective optimization. Many studies have been conducted on using these optimization problems in civil and construction engineering, especially for the various machine learning techniques and algorithms that have been developed for fiber reinforced polymer (FRP) applications in the rehabilitation and design of RC structures. FRP is considered the most effective and superior technique for strengthening and retrofitting due to its significant benefits over traditional methods, which have numerous drawbacks, as well as the importance of structural strengthening as a cost-effective and practical option. In this research, an insight into how to apply algorithms and machine learning approaches to optimize FRP applications in civil and construction engineering is presented, as well as a detailed analysis of the various optimization strategies used and their findings. A total of 18 case studies from previous research were discussed and critically evaluated, and they were categorized into six groups according to the algorithm or machine learning technique utilized. Based on the case studies investigated in this study, the genetic algorithm was found to be the optimal algorithm utilized for optimizing FRP applications. The result of this research provides a useful guideline for future researchers and specialists. [ABSTRACT FROM AUTHOR]

With the exponential growth of the amount of data being generated, stored and processed on a daily basis in the machine learning, data analytics and decision-making systems, the data preprocessing established itself as the key factor for building reliable high-performance machine learning models. One of the roles in preprocessing is variable reduction using feature selection methods; however, the processing time needed for these methods is a major drawback. This study aims at mitigating this problem by migrating the algorithm to a MapReduce implementation suitable for parallelization on a high number of commodity hardware units. The genetic algorithm-based methods were put in the focus of this study. Hadoop, an open-source MapReduce library, was used as a framework for implementing parallel genetic algorithms within our research. The representative machine learning methods, SVM (support vector machine), ANN (artificial neural network), RT (random tree), logistic regression and Naive Bayes, were embedded into implementation for feature selection. The feature selection methods were applied to four NSL-KDD data sets, and the number of features is reduced from cca 40 to cca 10 data sets with the accuracy of 90.45%. These results have both significant practical and theoretical impact. On the one hand, the genetic algorithm has been parallelized in the MapReduce manner, which has been considered unachievable in a strict sense. Furthermore, the genetic algorithm allows randomness-enhanced feature selection and its parallelization reduces overall data preprocessing and allows larger population count which in turn leads to better feature selection. On the practical side, it has been shown that this implementation outperforms the existing feature selection methods. [ABSTRACT FROM AUTHOR]

In this work, we propose the use of a genetic-algorithm-based attack against machine learning classifiers with the aim of 'stealing' users' biometric actigraphy profiles from health related sensor data. The target classification model uses daily actigraphy patterns for user identification. The biometric profiles are modeled as what we call impersonator examples which are generated based solely on the predictions' confidence score by repeatedly querying the target classifier. We conducted experiments in a black-box setting on a public dataset that contains actigraphy profiles from 55 individuals. The data consists of daily motion patterns recorded with an actigraphy device. These patterns can be used as biometric profiles to identify each individual. Our attack was able to generate examples capable of impersonating a target user with a success rate of 94.5%. Furthermore, we found that the impersonator examples have high transferability to other classifiers trained with the same training set. We also show that the generated biometric profiles have a close resemblance to the ground truth profiles which can lead to sensitive data exposure, like revealing the time of the day an individual wakes-up and goes to bed. [ABSTRACT FROM AUTHOR]