Large Number Of Attributes Research Articles

Comparing Two Deep-Learning Models in Discrete Choice Analysis of Customers’ Mobile Plan Preferences

A discrete choice experiment (DCE) was used to investigate students’ preferences for mobile phone plans at a South African university. Upon obtaining the data, this study compares the predictive performance of two machine-learning models for discrete choice analysis and makes recommendations for model selection. Using concepts from blocked fractional factorial designs, a locally optimal DCE was created for the choice sets. This contrasts with alternative ways that, in practice, could be more difficult, especially when there is a large number of attributes. The call rate, data speed, customer service, premiums, and network coverage were the features considered. A total of 180 respondents were chosen from the student population using a two-stage sample approach, and data were gathered through face-to-face interviews. In this study, two deep-learning models are examined to analyze the data, which are the artificial neural network (ANN) and the extreme gradient boosting (XGBoost) models. Root mean square error (RMSE) and mean absolute error (MAE) are used to assess the model fitness, while accuracy, precision, recall and F1 score were used to compare the models’ performance. The results showed that XGBoost performs better compared to ANN in model fitness and prediction. Thus, the use of the XGBoost deep-learning model in choice preference modeling is therefore encouraged.

Concepts in Context: Ontological Coherence in Political Science Research

Political methodologists have long sought to develop standards that can guide political scientists in the process of concept formation. Yet, the methodology literature has struggled to provide satisfactory solutions to the fundamental problem of conceptualization: for any given concept, there are a large number of attributes one could postulate as its defining characteristics, and it is unclear how to adjudicate between different possible definitions. We leverage the fact that the theory within which a concept appears places important restrictions on concept formation: conceptualizations entail ontological claims that need to be consistent with those of the theories in which they appear.

Key driver analysis with relative weight analysis: A two-step approach

Key driver analysis (statistically inferred importance analysis) is commonly implemented to understand what customer satisfaction attributes are most important in driving overall customer satisfaction. Building on prior research, this article suggests that dominance analysis and relative weight analysis are the most appropriate statistical techniques for conducting key driver analysis, yet relative weight analysis is the more feasible choice. A process is put forth so that research practitioners can conduct key driver analysis with relative weight analysis implementing a two-step approach. The two-step approach should be used when the key driver analysis contains a large number of attributes, which are theoretically redundant and highly correlated.

K-NN with Purity Algorithm to Enhance the Classification of the Air Quality Dataset

The large number of attributes in a large dataset can cause a decrease in the level of classification accuracy. Attribute reduction can be a solution to improve classification performance, especially in the K-NN algorithm. This research discusses the classification results of K-NN with attribute reduction using Purity. Based on the results of testing carried out on the Air Quality Dataset, the level of accuracy obtained after attribute reduction was 70.71%, while the level of accuracy obtained before attribute reduction was 56.44%, the increase in accuracy obtained from testing this dataset was equal to 14.27%. The proposed Purity method for attribute reduction can increase the accuracy level of the K-NN classification process.

Detection of Prostate Cancer using Ensemble based Bi-directional Long Short Term Memory Network

Aim and Background: In recent periods, micro-array data analysis using soft computing and machine learning techniques gained more interest among researchers to detect prostate cancer. Due to the small sample size of micro-array data with a larger number of attributes, traditional machine learning techniques face difficulty detecting prostate cancer. Methodology: The selection of relevant genes exploits useful information about micro-array data, which enhances the accuracy of detection. In this research, the samples are acquired from the gene expression omnibus database, particularly related to the prostate cancer GEO IDs such as GSE 21034, GSE 15484 and GSE 3325/GSE 3998. In addition, ensemble feature optimization technique and Bidirectional Long Short Term Memory (Bi-LSTM) network are employed for detecting prostate cancer from the microarray data of gene expression. Results: The ensemble feature optimization technique includes 4 metaheuristic optimizers that select the top 2000 genes from each GEO IDs, which are relevant to prostate cancer. Next, the selected genes are given to the Bi-LSTM network for classifying the normal and prostate cancer subjects. Conclusion: The simulation analysis revealed that the ensemble based Bi-LSTM network obtained 99.13%, 98.97%, and 94.12% of accuracy on the GEO IDs like GSE 3325/GSE 3998, GSE 21034, and GSE 15484.

Performance assessment of hybrid machine learning approaches for breast cancer and recurrence prediction.

Breast cancer is a major health concern for women everywhere and a major killer of women. Malignant tumors may be distinguished from benign ones, allowing for early diagnosis of this disease. Therefore, doctors need an accurate method of diagnosing tumors as either malignant or benign. Even if therapy begins immediately after diagnosis, some cancer cells may persist in the body, increasing the risk of a recurrence. Metastasis and recurrence are the leading causes of death from breast cancer. Therefore, detecting a return of breast cancer early has become a pressing medical issue. Evaluating and contrasting various Machine Learning (ML) techniques for breast cancer and recurrence prediction is crucial to choosing the best successful method. Inaccurate forecasts are common when using datasets with a large number of attributes. This study addresses the need for effective feature selection and optimization methods by introducing Recursive Feature Elimination (RFE) and Grey Wolf Optimizer (GWO), in response to the limitations observed in existing approaches. In this research, the performance evaluation of methods is enhanced by employing the RFE and GWO, considering the Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC) datasets taken from the UCI-ML repository. Various preprocessing techniques are applied to raw data, including imputation, scaling, and others. In the second step, relevant feature correlations are used with RFE to narrow down candidate discriminative features. The GWO chooses the best possible combination of attributes for the most accurate result in the next step. We use seven ML classifiers in both datasets to make a binary decision. On the WDBC and WPBC datasets, several experiments have shown accuracies of 98.25% and 93.27%, precisions of 98.13% and 95.56%, sensitivities of 99.06% and 96.63%, specificities of 96.92% and 73.33%, F1-scores of 98.59% and 96.09% and AUCs of 0.982 and 0.936, respectively. The hybrid approach's superior feature selection improved the accuracy of breast cancer performance indicators and recurrence classification.

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction of AD might be useful to mitigate or at-least slow down its progression for which, identification of the factors affecting the AD and its accurate diagnoses, are vital. In this study, we use Genome-Wide Association Studies (GWAS) dataset which comprises significant genetic markers of complex diseases. The original dataset contains large number of attributes (620901) for which we propose a hybrid feature selection approach based on association test, principal component analysis, and the Boruta algorithm, to identify the most promising predictors of AD. The selected features are then forwarded to a wide and deep neural network models to classify the AD cases and healthy controls. The experimental outcomes indicate that our approach outperformed the existing methods when evaluated on standard dataset, producing an accuracy and f1-score of 99%. The outcomes from this study are impactful particularly, the identified features comprising AD-associated genes and a reliable classification model that might be useful for other chronic diseases.

Real-world K-Anonymity applications: The KGen approach and its evaluation in fraudulent transactions

K-Anonymity is a property for the measurement, management, and governance of the data anonymization. Many implementations of k-anonymity have been described in state of the art, but most of them are not practically usable over a large number of attributes in a “Big” dataset, i.e., a dataset drawing from Big Data. To address this significant shortcoming, we introduce and evaluate KGen, an approach to K-anonymity featuring meta-heuristics, specifically, Genetic Algorithms to compute a permutation of the dataset which is both K-anonymized and still usable for further processing, e.g., for private-by-design analytics. KGen promotes such a meta-heuristic approach since it can solve the problem by finding a pseudo-optimal solution in a reasonable time over a considerable load of input. KGen allows the data manager to guarantee a high anonymity level while preserving the usability and preventing loss of information entropy over the data. Differently from other approaches that provide optimal global solutions compatible with smaller datasets, KGen works properly also over Big datasets while still providing a good-enough K-anonymized but still processable dataset. Evaluation results show how our approach can still work efficiently on a real world dataset, provided by Dutch Tax Authority, with 47 attributes (i.e., the columns of the dataset to be anonymized) and over 1.5K+ observations (i.e., the rows of that dataset), as well as on a dataset with 97 attributes and over 3942 observations.

Majority Voting as Ensemble Classifier for Cervical Cancer Classification

Cervical cancer is one of the deadliest female cancers. Early identification of cervical cancer through pap smear cell image evaluation is one of the strategies to reduce cervical cancer cases. The classification methods that are often used are SVM, MLP, and K-NN. The weakness of the SVM method is that it is not efficient on large datasets. Meanwhile, in the MLP method, large amounts of data can increase the complexity of each layer, thereby affecting the duration of the weighting process. Moreover, the K-NN method is not efficient for data with a large number of attributes. The ensemble method is one of the techniques to overcome the limitations of a single classification method. The ensemble classification method combines the performance of several classification methods. This study proposes an ensemble method with the majority voting that can be used in cervical cancer classification based on pap smear images in the Herlev dataset. Majority Voting is used to integrate test results from the SVM, MLP, and KNN methods by looking at the majority results on the test data classification. The results of this study indicate that the accuracy results obtained in the ensemble method increased by 1.72% compared to the average accuracy value in SVM, MLP, and KNN. for sensitivity results, the results of the ensemble method were able to increase the sensitivity increase by 0.74% compared to the average of the three single classification methods. for specificity, the ensemble method can increase the specificity results by 3.4%. From the results of the study, it can be concluded that the ensemble method with the most votes is able to improve the classification performance of the single classification method in classifying cervical cancer abnormalities with pap smear images.

On-Tube Attribute Visualization for Multivariate Trajectory Data.

Stylized tubes are an established visualization primitive for line data as encountered in many scientific fields, ranging from characteristic lines in flow fields, fiber tracks reconstructed from diffusion tensor imaging, to trajectories of moving objects as they arise from cyber-physical systems in many engineering disciplines. Typical challenges include large data set sizes demanding for efficient rendering techniques as well as a large number of attributes that cannot be mapped simultaneously to the basic visual attributes provided by a tube-based visualization. In this work, we tackle both challenges with a new on-tube visualization approach. We improve recent work on high-quality GPU ray casting of Hermite spline tubes supporting ambient occlusion and extend it by a new layered procedural texturing technique. In the proposed framework, a large number of data set attributes can be mapped simultaneously to a variety of glyphs and plots that are embedded in texture space and organized in layers. Efficient rendering with minimal data transfer is achieved by generating the glyphs procedurally and drawing them in a deferred shading pass. We integrated these techniques in a prototype visualization tool that facilitates flexible mapping of data set attributes to visual tube and glyph attributes. We studied our approach on a variety of example data from different fields and found it to provide a highly adaptable and extensible toolbox to quickly craft tailor-made tube-based trajectory visualizations.

Wisdom of the contexts: active ensemble learning for contextual anomaly detection

In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several “useful” contexts to unveil them. In this work, we propose a novel approach, called wisdom of the contexts (WisCon), to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. We estimate the importance of each context using an active learning approach with a novel query strategy. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active learning methods, unsupervised contextual and non-contextual anomaly detectors) on 18 datasets. Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the “wisdom” of multiple contexts is necessary.

Scalable Bayesian Approach for the Dina Q-Matrix Estimation Combining Stochastic Optimization and Variational Inference.

Diagnostic classification models offer statistical tools to inspect the fined-grained attribute of respondents' strengths and weaknesses. However, the diagnosis accuracy deteriorates when misspecification occurs in the predefined item-attribute relationship, which is encoded into a Q-matrix. To prevent such misspecification, methodologists have recently developed several Bayesian Q-matrix estimation methods for greater estimation flexibility. However, these methods become infeasible in the case of large-scale assessments with a large number of attributes and items. In this study, we focused on the deterministic inputs, noisy "and" gate (DINA) model and proposed a new framework for the Q-matrix estimation to find the Q-matrix with the maximum marginal likelihood. Based on this framework, we developed a scalable estimation algorithm for the DINA Q-matrix by constructing an iteration algorithm that utilizes stochastic optimization and variational inference. The simulation and empirical studies reveal that the proposed method achieves high-speed computation, good accuracy, and robustness to potential misspecifications, such as initial value choices and hyperparameter settings. Thus, the proposed method can be a useful tool for estimating a Q-matrix in large-scale settings.

Design of fuzzy rule-based models with fuzzy relational factorization

This study opens an original avenue of designing interpretable fuzzy rule-based models realized in the presence of data characterized by a large number of attributes (input variables). In the presence of such high-dimensional data, the development of rule-based models faces serious challenges both at the design and interpretability level. To address these two problems, we develop a new architecture of the rules and establish a two-step design process. First, the dimensionality reduction is realized with the aid of a fuzzy relational matrix factorization which transforms original fuzzy set-based encoded variables and yields a logic-oriented level of activation of individual rules. The results of relational factorization are logic expressions built over the encoded input variables forming condition parts of the rules. Second, the conclusions of the rules are built by optimizing local functions (both constant and linear functions are considered). A number of experiments are reported along with comparative studies involving a standard Takagi-Sugeno (TS) model.

HFMOEA: a hybrid framework for multi-objective feature selection

Abstract In this data-driven era, where a large number of attributes are often publicly available, redundancy becomes a major problem, which leads to large storage and computational resource requirement. Feature selection is a method for reducing the dimensionality of the data by removing such redundant or misleading attributes. This leads to a selection of optimal feature subsets that can be used for further computation like the classification of data. Learning algorithms, when fitted on such optimal subsets of reduced dimensions, perform more efficiently and storing data also becomes easier. However, there exists a trade-off between the number of features selected and the accuracy obtained and the requirement for different tasks may vary. Thus, in this paper, a hybrid filter multi-objective evolutionary algorithm (HFMOEA) has been proposed based on the nondominated sorting genetic algorithm (NSGA-II) coupled with filter-based feature ranking methods for population initialization to obtain an optimal trade-off solution set to the problem. The two competing objectives for the algorithm are the minimization of the number of selected features and the maximization of the classification accuracy. The filter ranking methods used for population initialization help in faster convergence of the NSGA-II algorithm to the PF. The proposed HFMOEA method has been evaluated on 18 UCI datasets and 2 deep feature sets (features extracted from image datasets using deep learning models) to justify the viability of the approach with respect to the state-of-the-art. The relevant codes of the proposed approach are available at https://github.com/Rohit-Kundu/HFMOEA.

Bat algorithm optimisation technique for feature selection on different dimension of datasets


 The advanced of Information Technology has resulting in the generation of numerous datasets with different dimensions. However, dealing with multi-dimensional datasets which typically contain large number of attributes, p has cause problems to classification process. Classifying different dimensional numerical data is a difficult problem as dealing with various feature spaces, could cause the performance of supervised learning method to suffer from the curse of dimensionality. This condition eventually degrades both classification accuracy and efficiency. In a nutshell, not all attributes in the dataset can be used in the classification process since some features may lead to low performance of classifier. Feature selection (FS) is a good mechanism that minimises the dimensions of high-dimensional datasets and solve classification problems. This paper proposed Bat Algorithm (BA) for FS that were trained using a Support Vector Machine (SVM) classifier. The proposed algorithm was tested on six public datasets with different sizes and compared with other benchmark algorithms, such as Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). The experimental results indicated that the BA has outperformed the other two algorithms. In addition, the comparison details showed that binary BA is more competitive in terms of accuracy and the number of features when assessed on datasets with different sizes. 

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.

Application of TOPSIS Method for Decision Making in Selecting the Best New Car in Malaysia

Purchasing and selecting of vehicles especially cars in the market is a tough decision to make due to the variety of the operational and technical parameter specifications such as design, body shape, technology used, and many more. Therefore, selection procedure techniques are required in order to overcome this problem. In the decision-making model, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is one of the selection procedure techniques to be embraced into this study. This technique provides a base for decision-making processes where there are limited numbers of choices but each has a large number of attributes. In this project, the selected vehicles were ranked based on their segments. The best car for sedan is Proton Persona, Proton Iriz was ranked as the best hatchback, Perodua Alza is the best MPV and Perodua Aruz was ranked as the best SUV. This result was based on the main criteria which are cost and total displacement, then follow by seat capacity, boot capacity, and top speed of the vehicle.

Assessing Sustainable Consumption in Packaged Food in Indonesia: Corporate Communication Drives Consumer Perception and Behavior

Sustainable consumption has been addressed in the literature in recent years, especially in relation to changing from a traditional consumption to sustainable consumption. Reducing environmental impacts from waste generation has been the focal point of sustainable consumption. However, a large number of attributes has caused a complexity in understanding which attributes effectively enhance the consumption. In particular, sustainable consumption has been facing a negative trend due to low levels of knowledge about packaging’s environmental impact, failed communication which leads to misperception and irresponsible behavior. This study contributes to proposing a set of attributes for enhancing sustainable consumption in the Indonesian food industry, to fulfill the lack of understanding of the attribute interrelationships using qualitative information. This study proposes a set of attributes to enhance sustainable consumption with qualitative information by assessing the interrelationships among the attributes employing the Delphi method and fuzzy decision-making trial and evaluation laboratory method to provide causal and effect relationships. As a result, corporate communication, consumer perception, consumer behavior, and product packaging are major aspects for sustainable consumption enhancement. Corporate communication becomes a major driver to affect perception and behavior. This study proposes a managerial insight for the packaged food industry on improving several criteria including green marketing, green consumerism, verbal features, and importance of information.

Empirical Analysis of the US State Department’s Annual Trafficking in Persons Report – Insights for Policy-Makers

ABSTRACT The State Department’s annual Trafficking in Persons (TIP) Report is the U.S. Government’s principal diplomatic tool to engage foreign governments on human trafficking. Each year, the report evaluates efforts to counter human trafficking, assigning each country to a tier level. We evaluate the relative role of various factors predictive of tier-level assignments, including (a) legislated changes to the ranking system, (b) party to the Palermo Protocol, (c) reported numbers of convictions, prosecutions, and identified victims, (d) independent estimates of prevalence, and (e) sample indicators of governance and economic development. We use singular-value decomposition to identify the relative influence among multiple inter-related factors across a matrix of tier rankings for twelve years and 189 nations. Our analysis indicates that investments in democratic institutions and individual rights may be significantly more influential than law enforcement, and the traditional economic theory for TIP vulnerability may be an oversimplification. Most significantly, the large number of attributes with small but statistically significant correlations with TIP tier levels confirms that TIP has many causal relationships. We affirm the need for Countering TIP (CTIP) strategies to apply an ecosystem approach with geographically targeted interventions consistent with Situational Crime Prevention.

Sağlık Veri Setlerinde Öznitelik Seçiminin Sınıflandırma Performansına Etkisi

Nowadays, since data sets become very high-dimensional and specific with the data collected from different devices, attribute selection has an important pre-processing task in reducing data size in data mining. This study aims to improve classification performance by reducing the calculation time and cost by using attribute selection methods. Attribute selection methods are examined under three main headings; filter method, wrapper method, and embedded method. In the study, support vector machine, Naive Bayes and decision trees methods (J48) among the machine learning classification algorithms were used. Data sets were obtained from UCI and Kaggle databases. Accuracy values were calculated to compare the classification performances of the algorithms. WEKA version 3.8.3, R3.3.0 and Tableu programs were performed in all analyzes. After unnecessary features were extracted by using appropriate methods in the analysis; classification performances and run times of algorithms were calculated. Accuracy values increased to 87% for Colorectal Histology MNIST, 85% for Parkinson's disease, 97% for SCADI, 100% for HCC, and 78% for breast cancer after attribute selection. The algorithm with the highest performance was found as a wrapper method with decision trees (J48). While the fastest algorithm was filter method, the longest-running algorithm was the wrapper method. According to results, the performance improvement was higher in feature sets with a large number of attributes after selecting feature. As a result, low dimensional data sets may provide higher classification accuracy with lower calculation costs.