Including Multi-feature Interactions and Redundancy for Feature Ranking in Mixed Datasets

Shekar, Arvind Kumar; Bocklisch, Tom; Sánchez, Patricia Iglesias; Straehle, Christoph; Müller, Emmanuel

doi:10.1007/978-3-319-71249-9_15

Cited by 9 publications

(4 citation statements)

References 12 publications

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Section: Methodsmentioning

confidence: 99%

“…Generally, in predicting tool wear, features are extracted in the time, frequency and timefrequency domain (Cheng et al, 2020). Shekar et al 2017 use the relevance and redundancy (RaR) approach to rank the feature by considering more relevant features and avoiding redundant features. Kumar (2014) proposed the different feature selection methods for selecting the important and relevant features.…”

Section: Performance Evaluation For Tool Wear Predictionmentioning

confidence: 99%

“…Appropriate feature selection helps to improve the performance and prediction accuracy of the model. In the feature extraction method, selected features should carry a minimum redundancy is a significant factor for regression analysis, especially for high dimensional space (Shekar et al 2017). The clean and missing value-free data used for feature selection purpose that is one of the contributing factors that help to improve prediction accuracy.…”

Section: Feature Selectionmentioning

confidence: 99%

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Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Kumar

Kolekar

Kotecha

et al. 2022

IJQRM

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Purpose Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.Design/methodology/approachThis paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.FindingsThe R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.Originality/value The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

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Section: Methodsmentioning

confidence: 99%

Section: Performance Evaluation For Tool Wear Predictionmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

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Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Kumar

Kolekar

Kotecha

et al. 2022

IJQRM

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“…Different methods analyze the relationship among features, the class label, and the correlation among variables [23] and get feature importance scores in order to allow for a more aware use of machine learning by non-experts. Those scores are often not aware of correlations among variables, thus leading to a necessary integration of a redundancy awareness concept [19].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data

Balestra¹,

Huber²,

Mayr³

et al. 2022

Preprint

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Not all real-world data are labeled, and when labels are not available, it is often costly to obtain them. Moreover, as many algorithms suffer from the curse of dimensionality, reducing the features in the data to a smaller set is often of great utility. Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand. These scores can be based only on the distribution of variables and the quantification of their interactions. The previous literature, mainly investigating anomaly detection and clusters, fails to address the redundancy-elimination issue. We propose an evaluation of correlations among features to compute feature importance scores representing the contribution of single features in explaining the dataset's structure. Based on Coalitional Game Theory, our feature importance scores include a notion of redundancy awareness making them a tool to achieve redundancy-free feature selection. We show that the deriving features' selection outperforms competing methods in lowering the redundancy rate while maximizing the information contained in the data. We also introduce an approximated version of the algorithm to reduce the complexity of Shapley values' computations.

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Selection of Relevant and Non-Redundant Multivariate Ordinal Patterns for Time Series Classification

et al. 2018

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Including Multi-feature Interactions and Redundancy for Feature Ranking in Mixed Datasets

Cited by 9 publications

References 12 publications

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data

Selection of Relevant and Non-Redundant Multivariate Ordinal Patterns for Time Series Classification

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