Ensuring the reliability and efficiency of computer numerical control (CNC) machines is crucial for industrial production. Traditional anomaly detection methods often struggle with uncertainty in vibration data, leading to misclassifications and ineffective predictive maintenance. This study proposes rough long short-term memory (RoughLSTM), a novel hybrid model integrating rough set theory (RST) with LSTM to enhance anomaly detection in CNC machine vibration data. RoughLSTM classifies input data into lower, upper, and boundary regions using an adaptive threshold derived from RST, improving uncertainty handling. The proposed method is evaluated on real-world vibration data from CNC milling machines, achieving a classification accuracy of 94.3%, a false positive rate of 3.7%, and a false negative rate of 2.0%, outperforming conventional LSTM models. Moreover, the comparative performance analysis highlights RoughLSTM’s competitive or superior accuracy compared to CNN–LSTM and WaveletLSTMa across various operational scenarios. These findings highlight RoughLSTM’s potential to improve fault diagnosis and predictive maintenance, ultimately reducing machine downtime and maintenance costs in industrial settings.

In text classification, taking words in text documents as features creates a very high dimensional feature space. This is known as the high dimensionality problem in text classification. The most common and effective way to solve this problem is to select an ideal subset of features using a feature selection approach. In this paper, a new feature selection approach called Rough Information Gain (RIG) is presented as a solution to the high dimensionality problem. Rough Information Gain extracts hidden and meaningful patterns in text data with the help of Rough Sets and computes a score value based on these patterns. The proposed approach utilizes the selection strategy of the Information Gain Selection (IG) approach when pattern extraction is completely uncertain. To demonstrate the performance of the Rough Information Gain in the experimental studies, the Micro-F1 success metric is used to compare with Information Gain Selection (IG), Chi-Square (CHI2), Gini Coefficient (GI), Discriminative Feature Selector (DFS) approaches. The proposed Rough Information Gain approach outperforms the other methods in terms of performance, according to the results.

This research presents an innovative approach in text mining based on rough set theory. This study fundamentally utilizes the concept of symmetry from rough set theory to construct indiscernibility matrices and model uncertainties in data analysis, ensuring both methodological structure and solution processes remain symmetric. The effective management and analysis of large-scale textual data heavily relies on automated text classification technologies. In this context, term weighting plays a crucial role in determining classification performance. Particularly, supervised term weighting methods that utilize class information have emerged as the most effective approaches. However, the optimal representation of class–term relationships remains an area requiring further research. This study proposes the Rough Multivariate Weighting Scheme (RMWS) and presents its mathematical derivative, the Square Root Rough Multivariate Weighting Scheme (SRMWS). The RMWS model employs rough sets to identify information-carrying documents within the document–term–class space and adopts a computational methodology incorporating α, β, and γ coefficients. Moreover, the distribution of the term among classes is again effectively revealed. Comprehensive experimental studies were conducted on three different datasets featuring imbalanced-multiclass, balanced-multiclass, and imbalanced-binary class structures to evaluate the model’s effectiveness. The results show that RMWS and its derivative SRMWS methods outperform existing approaches by exhibiting superior performance on balanced and unbalanced datasets without being affected by class imbalance and number of classes. Furthermore, the SRMWS method is found to be the most effective for SVM and KNN classifiers, while the RMWS method achieves the best results for NB classifiers. These results show that the proposed methods significantly improve the text classification performance.

Massively amounts of text data have been created on the Internet due to the widespread use of platforms like social media. Text classification is one of the most frequently used techniques for extracting useful information from text data. One of the most fundamental problems in text classification is high dimensionality. In text classification, high dimensionality greatly reduces the success of classifiers while increasing their computational cost. The most effective way to overcome this problem is to select a subset of features comprising the most distinctive features across the entire feature space, with the help of a feature selector. This study presents a new filter feature selection approach called Multivariate Feature Selector (MFS) for text classification. The proposed approach calculates a score for each feature based on three knowledge structures: class-based, document-based, and document-class-based. These structures have been utilized to reveal hidden information at the class, document, and document-class levels. This enables a more precise and effective scoring calculation for each term. The proposed method (MFS) was tested on four different datasets, and micro-F1 and macro-F1 measures were used as performance evaluators to prove the method’s success in feature selection. It has been observed that MFS outperforms the main feature selection methods in the literature. While different classification results were obtained depending on the selected feature size, MFS showed superior performance in all selected sub-feature spaces.

High dimensionality and sparsity are the primary issues in text classification. Using feature selection approaches, the most effective way to solve the problem is to select a subset of features. The most common and effective methods used for this process are filter techniques. Various performance metrics such as Micro-F1, Macro-F1, and Accuracy are used to evaluate the performance of filter methods used for feature selection on datasets Such methods work depending on a classification algorithm. However, when selecting features in filter techniques, the information on the individual features is evaluated without considering the relationship between the features. In such an approach, the actual performance of the filter technique used in feature selection may not be determined. In such a case, it causes the existing methods to be insufficient in testing the validity of the proposed method. For this purpose, this study suggests a novel performance metric called Selection Error (SE) to determine the actual performance evaluation of filter techniques. The Selection Error metric allows us to analyze the information value of the selected features more accurately than existing methods without relying on a classifier. The feature selection performance of the filtering approaches was performed on six different datasets with both The Selection Error and traditional performance metrics. When the results are examined, it is seen that there is a strong relationship between the proposed performance metric and the classification performance metric results. The Selection Error aims to significantly contribute to the literature by demonstrating the success of filtering feature selection methods, regardless of classifier performance.