scholarly journals Bi-level dimensionality reduction methods using feature selection and feature extraction

2010 ◽  
Vol 4 (2) ◽  
pp. 33-38 ◽  
Author(s):  
Mr. Veerabhadrappa ◽  
Lalitha Rangarajan
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Reduction Methods

scholarly journals New Application of Life Rank Algorithm: A Case Study

2021 ◽  
Vol 23 (06) ◽  
pp. 438-447
Author(s):  
Neha Sharma ◽  
Dr. RashiAgarwal ◽  
Dr. NarendraKohli ◽  
Dr. Shubha Jain
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Learning To Rank ◽  
Feature Reduction ◽  
Linear Feature ◽  
Redundant Data ◽  
Reduction Techniques ◽  
Before And After ◽  
Reduction Methods

The past few years have seen the emergence of learning-to-rank (LTR) in the field of machine learning. In information acquiring the size of data is very large and empowering a learning-to-rank model on it will be a costly and time taking process. High dimension data leads to irrelevant and redundant data which results in overfitting. “Dimensionality reduction” methods are used to manage this issue. There are two-dimensionality reduction techniques namely feature selection and feature reduction. There is extensive research available on the algorithm for learning-to-rank but this not the case for dimensionality reduction approaches in LTR, despite its importance. Feature selection techniques for classification are directly used for ranking. To the best of our understanding, feature extraction techniques in the context of ranking problems are not explored much to date. So, we make an effort to fill this void and explore feature extraction in the context of LTR problems. The LifeRank algorithm is a linear feature extraction algorithm for ranking. Its performance is analyzed on RankSVM and Linear regression. It is not applied to other learning-to-rank algorithms. So, in this task, an attempt is made to study the effect of the application of the LifeRank algorithm on other LTR algorithms. LifeRank algorithm is applied on RankNet and RankBoost. Then, the performance of several LTR algorithms on the LETOR dataset is analyzed before and after feature extraction.

scholarly journals Research on the Feature Selection of Rolling Bearings’ Degradation Features

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Yaolong Li ◽  
Hongru Li ◽  
Bing Wang ◽  
He Yu ◽  
Weiguo Wang
Keyword(s):  
Feature Selection ◽  
Dimensionality Reduction ◽  
Root Mean Square ◽  
Sample Entropy ◽  
Remaining Useful Life ◽  
Mean Square ◽  
Rolling Bearings ◽  
Reduction Methods ◽  
Useful Life ◽  
Selection Of

The bearings’ degradation features are crucial to assess the performance degradation and predict the remaining useful life of rolling bearings. So far, numerous degradation features have been proposed. Many researchers have devoted to use dimensionality reduction methods to reduce the redundancy of those features. However, they have not considered the properties and similarity of those features. In this paper, we present a simple way to reduce dimensionality by classifying different features based on their trends. And the degradation features can be classified into two subdivisions, namely, uptrends and downtrends. In each subdivision, there exists visible trend similarity, and we have introduced two indexes to measure this similarity. By selecting the representative features of the subdivision, the multifeatures can be dimensionality reduced. Through the comparison, the root mean square and sample entropy are two good representatives of uptrend and downtrend features. This method gives an alternative way for dimensionality reduction of the rolling bearings’ degradation features.

A Study of Feature Selection and Dimensionality Reduction Methods for Classification-Based Phishing Detection System

2021 ◽  
Vol 11 (1) ◽  
pp. 1-35
Author(s):  
Amit Singh ◽  
Abhishek Tiwari
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Detection System ◽  
Parallel Evolution ◽  
Feature Selection Method ◽  
Sensitive Information ◽  
Selection Methods ◽  
Survey Paper ◽  
Reduction Methods

Phishing was introduced in 1996, and now phishing is the biggest cybercrime challenge. Phishing is an abstract way to deceive users over the internet. Purpose of phishers is to extract the sensitive information of the user. Researchers have been working on solutions of phishing problem, but the parallel evolution of cybercrime techniques have made it a tough nut to crack. Recently, machine learning-based solutions are widely adopted to tackle the menace of phishing. This survey paper studies various feature selection method and dimensionality reduction methods and sees how they perform with machine learning-based classifier. The selection of features is vital for developing a good performance machine learning model. This work is comparing three broad categories of feature selection methods, namely filter, wrapper, and embedded feature selection methods, to reduce the dimensionality of data. The effectiveness of these methods has been assessed on several machine learning classifiers using k-fold cross-validation score, accuracy, precision, recall, and time.

scholarly journals Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction

2019 ◽  
Vol 33 ◽  
pp. 2280-2287 ◽  
Author(s):  
Baokun He ◽  
Swair Shah ◽  
Crystal Maung ◽  
Gordon Arnold ◽  
Guihong Wan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Heuristic Search ◽  
Search Algorithm ◽  
A Priori ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Combinatorial Search ◽  
Heuristic Search Algorithm

The following are two classical approaches to dimensionality reduction: 1. Approximating the data with a small number of features that exist in the data (feature selection). 2. Approximating the data with a small number of arbitrary features (feature extraction). We study a generalization that approximates the data with both selected and extracted features. We show that an optimal solution to this hybrid problem involves a combinatorial search, and cannot be trivially obtained even if one can solve optimally the separate problems of selection and extraction. Our approach that gives optimal and approximate solutions uses a “best first” heuristic search. The algorithm comes with both an a priori and an a posteriori optimality guarantee similar to those that can be obtained for the classical weighted A* algorithm. Experimental results show the effectiveness of the proposed approach.

scholarly journals Feature extraction and gait classification in hip replacement patients on the basis of kinematic waveform data

2021 ◽  
Vol 13 (1) ◽  
pp. 177-186
Author(s):  
Carlo Dindorf ◽  
Wolfgang Teufl ◽  
Bertram Taetz ◽  
Stephan Becker ◽  
Gabriele Bleser ◽  
...  
Keyword(s):  
Feature Selection ◽  
Dimensionality Reduction ◽  
Sagittal Plane ◽  
Knee Kinematics ◽  
Significant Information ◽  
Waveform Data ◽  
Abnormal Gait ◽  
Acceleration Data ◽  
Reduction Methods ◽  
Optical Motion

Abstract Study aim: To find out, without relying on gait-specific assumptions or prior knowledge, which parameters are most important for the description of asymmetrical gait in patients after total hip arthroplasty (THA). Material and methods: The gait of 22 patients after THA was recorded using an optical motion capture system. The waveform data of the marker positions, velocities, and accelerations, as well as joint and segment angles, were used as initial features. The random forest (RF) and minimum-redundancy maximum-relevance (mRMR) algorithms were chosen for feature selection. The results were compared with those obtained from the use of different dimensionality reduction methods. Results: Hip movement in the sagittal plane, knee kinematics in the frontal and sagittal planes, marker position data of the anterior and posterior superior iliac spine, and acceleration data for markers placed at the proximal end of the fibula are highly important for classification (accuracy: 91.09%). With feature selection, better results were obtained compared to dimensionality reduction. Conclusion: The proposed approaches can be used to identify and individually address abnormal gait patterns during the rehabilitation process via waveform data. The results indicate that position and acceleration data also provide significant information for this task.

scholarly journals FSEFST:Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data

2019 ◽  
Vol 9 (2) ◽  
pp. 814-820
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Efficient Algorithm ◽  
High Dimensional Data ◽  
Suggested Method ◽  
High Dimensional ◽  
Feature Subset

Dimensionality reduction is one of the pre-processing phases required when large amount of data is available. Feature selection and Feature Extraction are one of the methods used to reduce the dimensionality. Till now these methods were using separately so the resultant feature contains original or transformed data. An efficient algorithm for Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data (FSEFST) has been proposed in order to select and extract the efficient features by using feature subset method where it will have both original and transformed data. The results prove that the suggested method is better as compared with the existing algorithm

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