Unsupervised Feature Selection through Fitness Proportionate Sharing Clustering

Yan, Xuyang; Homaifar, Abdollah; Awogbami, Gabriel; Girma, Abenezer

doi:10.1109/smc.2018.00237

Cited by 5 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiment involves a comparative analysis of the LTV feature selection with several prominent feature selection methods to assess its ability to appropriately rank the Iris dataset's features. For this purpose, Relief-F [26], unsupervised feature saliency (UFSA) [27], Laplacian Score [28], unsupervised feature selection through fitness proportionate sharing clustering (UFSFPS) [29], locally linear embedding (LLE) [30] and radial basis function neural networks (RBFNN) [31] were used. Table 2 shows how the proposed and other methods ranked the features in the Iris dataset.…”

Section: Experimental Results Using the Iris Datasetmentioning

confidence: 99%

Supervised Feature Selection based on the Law of Total Variance

Mustapa,

Senawi,

Wei

2023

MEKATRONIKA

View full text Add to dashboard Cite

Feature selection is a fundamental pre-processing step in machine learning that decreases data dimensionality by removing superfluous and irrelevant features. This study proposes a supervised feature selection method based on feature relevance by employing the law of total variance (LTV). Specifically, the LTV is used to quantify the relevance of features by analysing the association between features and class label. Six classifiers were employed to evaluate the performance and reliability of the proposed method pertaining to classification accuracy. The results proved that a feature subset given by the proposed method has the capability to achieve comparable classification accuracy to the full feature set when just half or less than half of the original features are retained. The proposed method was also proven to be versatile as it can achieves adequate classification accuracy with all six classifiers with different learning schemes. In addition, a comparison with a similar type of feature selection method (AmRMR) shows that the proposed method yields a more accurate classification.

show abstract

Section: Experimental Results Using the Iris Datasetmentioning

confidence: 99%

Supervised Feature Selection based on the Law of Total Variance

Mustapa,

Senawi,

Wei

2023

MEKATRONIKA

View full text Add to dashboard Cite

show abstract

“…The classification accuracy (CA) on the considered datasets is computed by using Decision Tree, KNN, and MLP which are tabulated in TABLE 3, TABLE 4, and TABLE 5 respectively. For comparison, eight state-of-the-art feature selection methods have been considered whose results are taken from [20][21][22]. In each table, there are 12 columns in which first column corresponds to the dataset name, columns 2 to10 (M1 to M9) present the classification accuracies attained by considered methods, column 11 indicates the minimum percentage of selected features by any of the compared methods (M1 to M8), and column 12 contains the percentage of selected features by the proposed method.…”

Section: Datasetmentioning

confidence: 99%

“…In literature, a substantial amount of work on unsupervised feature selection method can be found [20][21][22]. In [23], a method is proposed that partitions the considered feature set into distinct clusters in such a way that features in a cluster are highly similar, while features in different clusters are quite dissimilar.…”

Section: Introductionmentioning

confidence: 99%

A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data

Saxena¹,

Chugh²,

Sajid³

et al. 2022

AAIML

View full text Add to dashboard Cite

A novel feature selection approach is presented in this paper. Sammon’s Stress Function transforms the high dimension data to a lower dimension data set. A data set is divided into small partitions. The features are assigned randomly to these partitions. Using GA with Sammon Error as fitness value, a small, desired number of features are selected from every partition. The combination of the reduced subsets of the features from these partitions is again divided into small partitions. After a certain number of iterating the process, a desired small number of features is obtained. For experimental validation, the proposed method has been tested on 11 standard datasets with three classifiers namely, Decision Tree, MLP and KNN. The classification accuracies obtained by the proposed method is highest on most of the considered datasets against the results reported in literature. Moreover, the proposed method selects comparatively less number of features in comparison to considered methods. The optimistic results obtained from the proposed method justify its strength.

show abstract

“…Most of these studies in literature focused on rigorous data analysis that involves feature selection [10], [11], and statistical feature formation techniques. These processes are time-consuming, and some features may only exist in some vehicles, which limits the practicability of the work.…”

Section: Related Workmentioning

confidence: 99%

Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network

Girma

Yan

Homaifar

2019

2019 IEEE 31st International Conference on Tools With Artificial Intelligence (ICTAI)

Self Cite

View full text Add to dashboard Cite

Despite advancements in vehicle security systems, over the last decade, auto-theft rates have increased, and cybersecurity attacks on internet-connected and autonomous vehicles are becoming a new threat. In this paper, a deep learning model is proposed, which can identify drivers from their driving behaviors based on vehicle telematics data. The proposed Long-Short-Term-Memory (LSTM) model predicts the identity of the driver based on the individual's unique driving patterns learned from the vehicle telematics data. Given the telematics is time-series data, the problem is formulated as a time series prediction task to exploit the embedded sequential information. The performance of the proposed approach is evaluated on three naturalistic driving datasets, which gives high accuracy prediction results.The robustness of the model on noisy and anomalous data that is usually caused by sensor defects or environmental factors is also investigated. Results show that the proposed model prediction accuracy remains satisfactory and outperforms the other approaches despite the extent of anomalies and noiseinduced in the data.

show abstract

Unsupervised Feature Selection through Fitness Proportionate Sharing Clustering

Cited by 5 publications

References 19 publications

Supervised Feature Selection based on the Law of Total Variance

Supervised Feature Selection based on the Law of Total Variance

A Novel Unsupervised Feature Selection Approach Using Genetic Algorithm on Partitioned Data

Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural Network

Contact Info

Product

Resources

About