2024· International Journal of Machine Learning and Predictive Analytics· Vol 7, pp. 01-15· 0 citations
TL;DR
The research evaluates several popular machine learning algorithms, including Decision Trees, Support Vector Machines, Random Forests, Naïve Bayes, Artificial Neural Networks, and k-Nearest Neighbor classifiers and demonstrates that meta-learning significantly improves model recommendation accuracy compared to traditional trial-and-error approaches while reducing training time and computational overhead.
Abstract
Recent advances in machine learning have produced numerous predictive algorithms for classification, regression, and forecasting tasks. However, selecting the most suitable model for a specific dataset remains challenging, often requiring expert knowledge, extensive experimentation, and significant computational resources. To address this issue, automated model selection has emerged as an important research area within machine learning and intelligent decision-support systems. Meta-learning, or “learning to learn,” provides an effective solution by utilizing knowledge gained from previously analyzed datasets to predict the performance of learning algorithms on new datasets. It examines dataset characteristics, known as meta-features, and recommends appropriate machine learning models, thereby improving selection accuracy while reducing computational costs. This study proposes a comprehensive meta-learning framework for automated predictive model selection. The framework includes dataset characterization, meta-feature extraction, meta-dataset generation, algorithm evaluation, and meta-model construction. Statistical, information-theoretic, landmarking, and complexity-based features are used to describe datasets and train a meta-learning model capable of recommending suitable algorithms for new predictive tasks. The research evaluates several popular machine learning algorithms, including Decision Trees, Support Vector Machines, Random Forests, Naïve Bayes, Artificial Neural Networks, and k-Nearest Neighbor classifiers. Experimental results demonstrate that meta-learning significantly improves model recommendation accuracy compared to traditional trial-and-error approaches while reducing training time and computational overhead. As part of the broader field of Automated Machine Learning (AutoML), the proposed framework offers an intelligent algorithm recommendation system that supports efficient resource utilization and assists practitioners in selecting high-performing models without extensive machine learning expertise. The findings highlight the potential of meta-learning-based model selection for future intelligent analytics, decision-support, and large-scale data mining systems.
Artificial Intelligence (AI) has improved predictive modeling by enabling efficient analysis of large-scale and high-dimensional datasets. Before 2019, selecting relevant features from complex data was a major challenge. Feature selection is important for improving accuracy, reducing computational cost, preventing overfitting, and enhancing model interpretability. Traditional methods such as filter, wrapper, and embedded approaches had limitations with big data and nonlinear relationships. To address these issues, AI-based Automated Feature Selection (AFS) techniques were introduced. These methods combine machine learning, optimization algorithms, and intelligent search strategies to automatically identify the most relevant features. Common approaches included Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), and Reinforcement Learning (RL). This study reviews AI-driven feature selection techniques developed between 2009 and 2018 and proposes a hybrid framework combining filter-based preprocessing, evolutionary optimization, and machine learning evaluation. Experimental results show that AI-based hybrid methods outperform traditional techniques in accuracy, scalability, and feature optimization, demonstrating their importance in modern predictive analytics systems.
Fang Liu· International Journal of Mac...· 0 citations
Artificial Intelligence (AI) has significantly transformed data analytics by enabling intelligent and data-driven decision-making through advanced computational models. This study presents a comprehensive comparative evaluation of three widely used machine learning algorithms: Logistic Regression, Decision Tree, and Random Forest, for classification tasks. The analysis is performed on a structured dataset incorporating systematic preprocessing, feature engineering, and model optimization techniques. Model performance is evaluated using standard metrics, including accuracy, precision, recall, and F1-score. The experimental results demonstrate that Random Forest achieves superior generalization performance compared to the other models. The findings emphasize the critical role of selecting appropriate models based on dataset characteristics and provide valuable insights for future research in predictive analytics.
Badhur Ammulya, G. Nagalakshmi· International Journal of Lat...· 0 citations
Predictive analytics plays a crucial role in data science by forecasting future trends using historical data. Traditional statistical techniques such as linear regression, logistic regression, ARIMA, and Bayesian inference provide interpretable and mathematically rigorous models but often struggle with large-scale, complex, and nonlinear datasets. Machine learning approaches, including Decision Trees, Support Vector Machines, Random Forests, Artificial Neural Networks, and Ensemble Learning, offer superior predictive capabilities but may lack interpretability and uncertainty estimation. To address these limitations, hybrid statistical–machine learning methods have emerged, combining statistical feature engineering, probabilistic modeling, and machine learning algorithms to improve prediction accuracy and robustness. This paper reviews the theoretical foundations, architectures, and applications of hybrid predictive models across finance, healthcare, manufacturing, transportation, and business intelligence. A generalized hybrid framework incorporating feature selection, model training, ensemble optimization, and validation is presented. The analysis indicates that hybrid approaches consistently outperform standalone statistical and machine learning models in terms of accuracy, reliability, and generalization. Key implementation challenges, including computational complexity, interpretability, data quality, and parameter optimization, are also discussed. The study concludes that hybrid statistical–machine learning models represent a promising direction for next-generation predictive analytics and intelligent decision-support systems.
Arvind Kumar Singh· International Journal of Mac...· 0 citations
Hybrid machine learning offers a flexible approach for predictive analytics and intelligent decision-making in heterogeneous data environments. This study developed and evaluated Logistic Regression, Random Forest, soft voting, and weighted voting using a publicly available educational dataset comprising 4,424 student records, 36 predictors, and three outcome classes: dropout, enrolled, and graduate. Data preprocessing included categorical encoding, numerical scaling, class-weighted learning, and stratified training-testing separation. The models were compared using accuracy, balanced accuracy, macro precision, macro recall, macro F1-score, receiver operating characteristic area under the curve, log loss, and calibration measures. The weighted-voting ensemble achieved the best overall performance, with 76.72% accuracy, 71.89% balanced accuracy, 71.81% macro F1-score, and an area under the curve of 0.904. Performance improved from a macro F1-score of 55.54% with baseline predictors to 65.91% after first-semester information and 71.81% after second-semester information were added. Academic progression indicators, tuition-fee status, age at enrolment, debtor status, course, and gross domestic product were influential predictors. Probability thresholds supported high-priority intervention, moderate-risk monitoring, and uncertain-case review. The enrolled category remained the most difficult outcome to classify because of imbalance and transitional characteristics. Hybrid ensembles provided accurate, interpretable, and decision-oriented predictions, although external validation and prospective evaluation are required before institutional deployment.
Haleeful Jud· Journal of Intelligent Decis...· 0 citations
The feature selection is a crucial step in predictive analytics to determine which subset of features makes the most contribution to the high-dimensional data and remove irrelevant, redundant, or noisy features. The dimensionality of datasets keeps on growing, and, as contemporary data-driven applications produce large volumes of heterogeneous data, overfitting, computational complexity, worse model interpretability, and poorer generalization become issues as heterogeneous data increases. The feature selection methods are meant to address such challenges by improving predictive accuracy, minimizing training time and improving model robustness. This survey is a systematic and extensive overview of feature selection methods used in predictive analytics which are utilized in a variety of areas and fields, including healthcare, finance, bioinformatics, cybersecurity, and smart systems. In the paper, the features selection techniques have been classified as filter, wrapper, embedded, and hybrid techniques which give a comprehensive theoretical background of each of the techniques as well as a comparison of each of the techniques. Statistical, information-theoretic, similarity-based, and probabilistic filters are discussed in addition to the heuristic and metaheuristic wrapper methods, i.e. evolutionary, swarm-based etc. Also critically analyzed is embedded techniques that make use of regularization, decision trees, and ensemble learning. Moreover, this survey talks about the evaluation metrics, benchmark data, and design considerations of the experiment which are used in the evaluation of the effectiveness of the feature selection. Such practice issues as scalability, stability, data imbalance, and interpretability are mentioned, as well as new directions related to deep learning-based feature selection and multi-objective optimization and explainable artificial intelligence. This piece of work can be regarded as a useful source of information by the researcher and practitioners who want to develop effective, precise, and understandable predictive analytics systems.
Nesca Mthethwa, Thane Nkosi· International Journal of Mac...· 0 citations
The article examines an adaptive algorithm for automated selection and training of machine learning models, AIONet (Adaptive Intelligent Optimization Network), based on the integration of decision tree methods and large language models (LLMs). A comparative analysis is conducted of the capabilities of classical decision tree algorithms and modern LLMs in the task of identifying suitable models and datasets for automated neural network training. A hypothesis is formulated that combining a decision tree as a structure for primary logical selection with an LLM as a context-dependent intelligent module can improve the accuracy and efficiency of model and dataset selection compared to using each approach independently. The key features of the AIONet algorithm’s operation are presented, as well as its potential for application in automated machine learning systems.
S. A. Maslov, O. I. Zakharova· Infokommunikacionnye tehnolo...· 0 citations
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