Multi-Group Data Classification via MILP

Angiotensin I converting enzyme (ACE) gene, as a component of Renin-Angiotensin System (RAS), regulates blood pressure as it converts somatic isozyme Angiotensin I into physiologically active peptide Angiotensin II and simultaneously brakes down bradykinins. Over 100 polymorphisms are reported for ACE gene. Most of these polymorphisms having no phenotypic effect relay the attention towards polymorphisms based on insertions (I) or deletions (D) of a 287 bp Alu repeat sequence in 16th intron. There are three possible genotypes for the stated polymorphism: DD, DI or II. There have been a lot of studies searching for direct associations between ACE polymorphisms and performance phenotypes along different sports requiring power or endurance. The previous experiments are based on the performance criteria but direct associations of ACE polymorphisms are not fully understood until today. We believe different approaches may aid scientist to plot the big picture. A sample population of 101 individuals from Bosnia and Herzegovina contributed to sample pool of the initial project. Buccal swabs from 101 samples were collected along with a phenotypic structure and environmental characteristic survey which was filled by each individual himself/herself. Genotypes of the individuals were obtained after isolation, amplification and gel electrophoresis of biological samples collected as buccal swabs. A total of 165 artificial neural network (ANN) models were developed considering the input parameters, possible genotype outputs, applied algorithm and sample size. The aim of developing various ANNs was to validate a possible ACE polymorphism genotype prediction algorithm based on phenotypic and environmental characteristics of individuals, in other words, without any biological testing. A two-layered feed-forward network, with sigmoid hidden neurons was designed to perform the classification of input data. Trainscg (Scaled Conjugate Gradient) activation function was used in hidden layer since classification of data was non-linear. All ANN models were trained with scaled conjugate gradient backpropagation. ANN models differing in the parameters has shown different accuracy in the results. Most outstanding result was observed in the ANN build composed of 2 distinctive layers with 500 neurons in the first and 3 neurons in the second layer. Trained with 70% of samples and verified with 15% of samples and validated with an additional 50 samples. Training set was composed of the following subject parameters; gender, eye color, hair color, height, weight, presence of hypertension in family and presence of cardiovascular diseases in family. The highest prediction accuracy was obtained as 86,6 % training score, 78,6 % testing score and 80,2 % overall score in genotype prediction for ACE polymorphisms. With further development of data collection and high resolution analysis, overall score could be boosted. Also, phenotypical data can be applied as markers of genotypes in ACE polymorphisms.

Combining a new data classification technique and regression analysis to predict the Cost-To-Serve new customers

This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such as minelike objects, human figures or debris of wrecked ships). Not only is the contribution of this work to provide a systematic description of the state of the art of this field, but also to identify five main ingredients in its current development: the application of deep-learning methods using convolutional layers alone; deep-learning methods that apply biologically inspired feature-extraction filters as a preprocessing step; classification of data from frequency and time–frequency analysis; methods using machine learning to extract features from original signals; and transfer learning methods. This paper also describes some of the most important datasets cited in the literature and discusses data-augmentation techniques. The latter are used for coping with the scarcity of annotated sonar datasets from real maritime missions.

Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis

Multi-group classification arises in many prediction and decision-making problems, including applications in epidemiology, genomics, finance, and image recognition. Although classification methods have advanced considerably, much of the literature focuses on binary problems, and available extensions often provide limited flexibility for multi-group settings. Recent work has extended linear discriminant analysis to multiple groups, but more general methods are still needed to handle complex structures such as nonlinear decision boundaries and group-specific covariance patterns. We develop Multi-Group Quadratic Discriminant Analysis (MGQDA), a method for multi-group classification built on quadratic discriminant analysis. MGQDA projects high-dimensional predictors onto a lower-dimensional subspace, which enables accurate classification while capturing nonlinearity and heterogeneity in group-specific covariance structures. We derive theoretical guarantees, including variable selection consistency, to support the reliability of the procedure. In simulations and a gene-expression application, MGQDA achieves competitive or improved predictive performance compared with existing methods while selecting group-specific informative variables, indicating its practical value for high-dimensional multi-group classification problems. Supplementary materials for this article are available online.

Land Use/Land Cover (LULC) classification data have proven to be valuable assets for various governmental agencies, park managers, and natural resource managers. Traditional pixel-based classification methods have difficulty with high resolution imagery, resulting in a “salt and pepper” appearance. Newer object-based methods may prove to be more accurate. This study compared an object based classification procedure utilizing Feature Analyst© software with a traditional pixel-based methodology (supervised classification) when applied to medium-spatial resolution satellite imagery merged with high-spatial resolution aerial imagery. This study utilized two multi-spectral SPOT-5 satellite images, leaf-on and leaf-off, merged with a color infrared aerial image. Because of correlation between some of the bands of the merged image, Principal Component Analysis (PCA) was used to reduce redundancy in the data. Field data was collected in the study area to serve as a reference for the accuracy assessment. A training set was produced by selecting and identifying specific LULC class-types using 1-foot high-spatial resolution aerial imagery. This training set was used by both of the classification methods (supervised and object-based) to identify the various cover types within the study area. An accuracy assessment was performed on each image utilizing error matrices, the Kappa coefficient, and a two-tailed Z-test. Results indicate that the overall accuracy of the object-based classification was 82.0%, while the pixel-based classification was 66.9%. A Kappa analysis and a two tailed Z test were calculated. These values indicated a significant difference in the overall accuracies of the classifications.

This article explores the creation of a data classification policy in line with SOC2 Type 2 compliance requirements. SOC2 Type 2 is a notable certification that attests to an organization's ability to adhere to the Trust Services Criteria including security availability processing integrity confidentiality and privacy. The initial and crucial step in formulating a solid data security strategy is data classification which helps organizations recognize their data and assign a sensitivity level guiding the appropriate security measures. Data classification aims to organize and manage data in a manner that enhances its protection and aligns with the organization's overall data security strategy. In the data classification process data security has a central role as it directly impacts the protection and management of classified data. The design of a data classification policy for SOC2 Type 2 compliance presents several challenges and considerations. Organizations must understand the scope of their data align with the Trust Services Criteria balance security with usability provide training and awareness conduct regular updates and reviews define classification levels ensure consistency automate classification integrate with other policies and controls handle third-party vendors monitor and enforce and comply with legal and regulatory requirements. Keywords: SOC2 Type 2 storage standards data classification data storage data security.

Data mining techniques have been used by several researchers to detect illnesses. Some methods are intended to predict a single sickness, while others are intended to predict a wide variety of diseases. It is also possible to improve the accuracy of sickness prediction. In this post, we provided an overview of data classification approaches that are available. These algorithms are mostly represented by themselves. The classification of data is a common and computationally difficult procedure. A massive amount of information must be analyzed in order to come up with a sound strategy for dealing with disease. There are a number of common scenarios, including as early disease detection, severity assessment, and prediction. This will decrease the progression of the disease, improve the well-being of the patients, and reduce the associated costs of medical care. In this regard, machine learning techniques have been employed. This article presents a machine learning based framework for heart disease data classification and prediction.

The energy problem has gained great attention from the public in recent years. To construct a building energy consumption monitoring system is an effective way to find out the situation of the building energy consumption and go forward to make better decisions to solve the energy problem. Many related technologies and methods have been proposed, and this paper will focus on the data organization of the monitoring system. Firstly, a method to carry on data classification according to the features of the data is introduced, and then the tree structure is used for the data organization and data processing. The data is organized in ordinary tree structure in the database and on the webpages. While in data processing, it will be transformed to Binary tree. Meanwhile, the tree structure is also useful to solve the data security problem. After basic testing in the project of the building energy consumption monitoring system construction, this way of data organization has been shown to be effective.

This article discusses the practical aspects of applying logistic regression for binary data classification. Logistic regression determines the probability of an object belonging to one of two classes. This probability is calculated with the help of a sigmoid function, the argument of which is a linear convolution of the feature vector of the object with the weighting coefficients obtained during the minimization of the logarithmic loss function. Predicted class labels are determined by comparing the calculated probability with a given threshold value. The logistic regression study was performed using the computer simulation method. For this, a software complex was developed, the work of which reproduces the main stages of logistic regression: preparation of input data, training, testing with determination of quality metrics of binary classification, application of the logistic regression method for data classification in practice. The paper examines the effect of overlapping and imbalance of classes in the input data set on the efficiency of binary classification. The overlapping of classes is modeled by the formation of input data based on two shifted relative to each other density functions of the normal distribution of random variables. Class imbalance is simulated by the probability of switching between these features. It is shown that when the distance between the mathematical expectations of the density functions of the normal distribution decreases or when the dispersion of random variables increases, the overlapping of relevant classes increases, which leads to an increase in the number of objects that the classifier can assign to one or another class. Approaching the probability of switching between the distribution functions of random variables to the extreme values of the unit interval leads to an increase in class imbalance, which is manifested in an increase in the number of elements of the input data set labeled with the label of the same class. It has been experimentally confirmed that the AUC ROC metric, popular in binary classification problems, is dependent on the degree of class overlap and relatively resistant to class imbalance.

Rapid improvements in the world of information technology have resulted in a demand for data collecting and the act of storing data for future retrieval. As a result, many businesses and organisations are driven to collect and store massive amounts of data in the data warehouse, a recently built database module. Data classification is a strategy for building a data classifier model that describes the relevant data classes and allows for a better comprehension of the data. It is important to highlight that this data classification approach is not well defined and is a non-deterministic process because there is no guarantee that the data classifier model constructed from past data would work better for future data. In the light of these facts, this chapter discusses the data classification method and its usefulness in the engineering and science fields. The significance and need for a research contribution for data classification using various machine learning models with optimization methods are highlighted, bringing the research work’s aims to light. In this chapter, the various approaches used for designing and developing classifier models, as well as their benefits and drawbacks, are discussed, as well as the hybrid support vector machine with grey wolf optimization (HSVMGWO) used to classify the various datasets. After a thorough evaluation, the proposed HSVMGWO method outperformed the four types of conventional methods: random forest, support vector machine, ada boost algorithm, and finally decision tree, with an efficiency of 95.3 percent, sensitivity of 97.08 percent, specificity of 95.68 percent, and time responsibility of 13.186 M.sec. Statistical study of accuracy values and calculation time shows that the proposed schemes outperform existing approaches.KeywordsGrey wolf optimizationSupport vector machineMultivariate datasetRandom forestAda boost algorithmDecision tree

For more than five decades, marketing has been an important field of application for a multitude of data analysis and classification methods. The sophisticated collection of adequate data from and about customers and competitors as well as the purposeful application of methods like cluster, discriminant, factor and regression analysis, multidimensional scaling and association analysis have become widely accepted standards in the treatment of marketing problems. Decision support is provided in that way for market segmentation, market structuring, target marketing/advertising, new product development, pricing, positioning, and cross-media communication, to mention only a few. The lessons learned from small and large-scale applications have motivated marketing researchers to develop dedicated methods and to further improve existing methods with regard to their fields of application. Since the theoretical and methodological standards in the fields of interest are very high today, promising adoptions of data analysis and classification methods from other application fields or directly from theory are challenges of continuous importance. The present Special Issue of ADAC highlights recent developments regarding the mentioned aspects in marketing dealing with topics like

Machine Learning (ML) is widely applied to automate repetitive tasks, offering potential benefits across various domains. These include safety-sensitive applications such as the analysis of polysomnographies in sleep medicine. Although there are unmistakably many benefits of using machine learning for sleep analyses, concerns have been raised about the accuracy and robustness of the proposed models. This prevents the models from being integrated in the medical routines. We draw inspiration from concrete challenges posed by two applications in sleep analysis: sleep stage scoring and respiratory analysis. We propose multiple machine learning approaches for enhancing the trustworthiness and reliability of ML models. Moreover, we illustrate their efficacy on the two aforementioned medical tasks. We focus on three different aspects: the explainability of the model, the ambiguity of labels and predictions, and the robustness of the model. The primary barrier is the lack of explainability of the developed models that leads medical experts to be cautious about incorporating new learned models into their workflows. Because medical experts retain ultimate responsibility for care, even when models are accurate and time-saving, the lack of transparency (e.g., about what data was used to train the model, how it arrives at its prediction) still represents a significant hurdle. We address this concern in sleep stage scoring by proposing a classifier which mimics the way humans score which makes it simple to explain. Furthermore, we developed a novel method for multiple sequence alignment on sequences of item sets. This enables identifying pathological breathing patterns such as apnea and hypopnea without the need for complicated feature construction or classification methods. The second barrier is the ambiguity of the class labels in the data. Despite having access to a sufficiently large training set, an ML model can still make systematic mistakes. For some observations, it may, for example, not be possible to unambiguously associate a single class label such as for observations in a transition region between two classes. Moreover, the training data can contain too many incorrectly labeled examples. This ambiguity has two consequences which we tackle. First, there are examples for which the model is not able to predict a single class with high certainty. We resolve this by introducing a reject option into the sleep stage classifier, i.e., we allow the classifier to abstain from predicting a sleep stage if it is too uncertain in its prediction. Second, it is not possible to evaluate the model using standard techniques when the true class label for a test example is not unique. Hence, we developed an alternative performance evaluation that takes both the prediction and label uncertainty into account. The third barrier is the lack of robustness present in ML models when they are forced to extrapolate to regions of the feature space that were not (sufficiently) present in the training data. Such extrapolation is omnipresent in many practical cases in which a model is (unintentionally) used on data that deviates from the training set. In sleep medicine, for example, the data is affected by factors such as the patient’s age, gender and pathologies. Hence, it is extremely difficult, if not impossible, to ensure that the training data is sufficiently diverse and exhaustive. We show that an anomaly detector is useful to identify deviating patients in sleep stage scoring. However, this approach results in a sub-optimal rejection performance when applied on individual predictions, i.e., the reject option is often too aggressive in practice as there are many seemingly anomalous patterns which are easy to classify accurately. This is, for example, the case when a patient is moving. In that case, multiple sensors register signals with very high and unique amplitudes while movement is often associated with wakefulness. Therefore, we introduce a new two-step rejection framework which includes a dependency between the reject option and the classifier itself to overcome this problem.

Classification is a machine learning procedure that tags data instances into predefined class labels which are used to predict the data according to those Classes. Many classification algorithms in data mining have been stated, such as: C4.5, Apriori, Genetic algorithm, and Fuzzy set approaches which mainly uses heuristic or greedy search to get frequent sets in data for classification, resulting in high error ratio. Recently, a new method for classification has been proposed, called the Classification Using Association also known as associative classification. The main purpose for this is to mine class-association rules. Associative Classification has more advantages than the heuristic and greedy method, as it easily removes noise and higher accuracy is obtained. It additionally generates a rule set, that are more complete than traditional classification methods. This paper presents a meticulous survey on various Associative classification techniques. Moreover, a comparative analysis of accuracy and efficiency of those methods is presented.

Data classification and tag recommendation are both important and challenging tasks in social media. These two tasks are often considered independently and most efforts have been made to tackle them separately. However, labels in data classification and tags in tag recommendation are inherently related. For example, a Youtube video annotated with NCAA, stadium, pac12 is likely to be labeled as football, while a video/image with the class label of coast is likely to be tagged with beach, sea, water and sand. The existence of relations between labels and tags motivates us to jointly perform classification and tag recommendation for social media data in this paper. In particular, we provide a principled way to capture the relations between labels and tags, and propose a novel framework CLARE, which fuses data CLAssification and tag REcommendation into a coherent model. With experiments on three social media datasets, we demonstrate that the proposed framework CLARE achieves superior performance on both tasks compared to the state-of-the-art methods.