CFS Based Feature Subset Selection for Enhancing Classification of Similar Looking Food Grains- A Filter Approach

Pushpalatha, K.R.; Karegowda, Asha Gowda

doi:10.1109/icecit.2017.8453403

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…Saeys et al [4] show that the various FS methods have given impressive results in the field of bioinformatics. Using Weka tool, Pushpalatha and Karegowda [8] perform CFS based filter approach to rank with five search techniques. Dash et al [9] choose the best feature subset for clustering by evaluating the various subsets of features.…”

Section: Related Work a Filter Methodsmentioning

confidence: 99%

Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method

Thejas

Joshi²,

Iyengar

et al. 2019

IEEE Access

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Feature Selection has been a significant preprocessing procedure for classification in the area of Supervised Machine Learning. It is mostly applied when the attribute set is very large. The large set of attributes often tend to misguide the classifier. Extensive research has been performed to increase the efficacy of the predictor by finding the optimal set of features. The feature subset should be such that it enhances the classification accuracy by the removal of redundant features. We propose a new feature selection mechanism, an amalgamation of the filter and the wrapper techniques by taking into consideration the benefits of both the methods. Our hybrid model is based on a two phase process where we rank the features and then choose the best subset of features based on the ranking. We validated our model with various datasets, using multiple evaluation metrics. Furthermore, we have also compared and analyzed our results with previous works. The proposed model outperformed many existent algorithms and has given us good results. INDEX TERMS Feature selection, filter method, hybrid feature selection, normalized mutual information, mini batch K-means, random forest, wrapper method.

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Section: Related Work a Filter Methodsmentioning

confidence: 99%

Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method

Thejas

Joshi²,

Iyengar

et al. 2019

IEEE Access

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“…One of drawbacks is that IG methods do not consider the relations among features [7][8] [9] . CFS (correlation based feature selection) algorithm considers interacting features, based on the hypothesis that good features have a high correlation with the category, yet no relevance to each other [10] . However, the CFS method only considers the correlation of features and categories, and it is not ideal for the processing of nonlinear features and noise data [11] .…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Ensemble feature selection with adaptive weights

He,

Li,

Guo

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Most of the existing ensemble feature selection algorithms directly assign different feature selection algorithms the same weights or simply treat accuracy as weight and then vote for each feature. However, these research methods either ignored or did not fully consider the fitness of the feature selecttion algorithm to the data set. To deal with this challenge, we propose a new ensemble feature selection approach with adaptive weights. In detail, we use the softmax function to dynamically adjust the weight of the base feature selector according to its fitness to dataset in the k-round training process. We name it ensemble feature selection based on softmax function (EFS-BSF) algorithm. We demonstrate the superiority of the EFS-BSF approach over previous methods through mathematical analysis and experiments on multiple data sets.

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“…In [4], an FS technique based on correlation is proposed, in which the features are ranked based on the extent of redundancy between the attributes and their predictive capability. Kira and Rendell created the FS technique called Relief [5].…”

Section: A Filter Approachmentioning

confidence: 99%

Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches

Thejas¹,

Garg²,

Iyengar

et al. 2021

IEEE Access

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Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some filter or by using the performance of the learning model to gauge the quality of the attribute subset. Metric Ranked Feature Inclusion and Accuracy Ranked Feature Inclusion are the two novel hybrid feature selection methods we introduce in this paper. These algorithms follow a two-stage procedure, the first of which is feature ranking, followed by feature subset selection. They differ in the way they rank the features but follow the same subset selection technique. Multiple experiments have been conducted to assess our models. We compare our results with numerous works of the past and validate our models using 12 datasets. From the results, we infer that our algorithms perform better than many existent models.

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CFS Based Feature Subset Selection for Enhancing Classification of Similar Looking Food Grains- A Filter Approach

Cited by 14 publications

References 15 publications

Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method

Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method

Ensemble feature selection with adaptive weights

Metric and Accuracy Ranked Feature Inclusion: Hybrids of Filter and Wrapper Feature Selection Approaches

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