scholarly journals A Simple Feature Reduction Method for the Detection of Long Biological Signals

2005 ◽  
pp. 431-439
Author(s):  
Max Chacón ◽  
Sergio Jara ◽  
Carlos Defilippi ◽  
Ana Maria Madrid ◽  
Claudia Defilippi
Keyword(s):  
Reduction Method ◽  
Feature Reduction ◽  
Biological Signals

scholarly journals An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Hakan Gunduz
Keyword(s):  
Reduction Method ◽  
Feature Reduction ◽  
The Other ◽  
Recursive Feature Elimination ◽  
Accuracy Rate ◽  
Stock Performance ◽  
Attention Model ◽  
Feature Sets ◽  
Accuracy Rates ◽  
F Measure

AbstractIn this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models. These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics. While the first experiments directly used the own stock features as the model inputs, the second experiments utilized reduced stock features through Variational AutoEncoders (VAE). In the last experiments, in order to grasp the effects of the other banking stocks on individual stock performance, the features belonging to other stocks were also given as inputs to our models. While combining other stock features was done for both own (named as allstock_own) and VAE-reduced (named as allstock_VAE) stock features, the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination. As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model, the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675. Although the classification results achieved with both feature types was close, allstock_VAE achieved these results using nearly 16.67% less features compared to allstock_own. When all experimental results were examined, it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features. It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.

A proposed scheme for sentiment analysis

Kybernetes ◽  
2018 ◽  
Vol 47 (5) ◽  
pp. 957-984 ◽  
Author(s):  
Sajjad Tofighy ◽  
Seyed Mostafa Fakhrahmad
Keyword(s):  
Sentiment Analysis ◽  
Clustering Algorithm ◽  
Reduction Method ◽  
Feature Reduction ◽  
Sentiment Classification ◽  
Support Vector ◽  
Second Phase ◽  
Reduction Algorithm ◽  
Content Type ◽  
Statistical Knowledge

Purpose This paper aims to propose a statistical and context-aware feature reduction algorithm that improves sentiment classification accuracy. Classification of reviews with different granularities in two classes of reviews with negative and positive polarities is among the objectives of sentiment analysis. One of the major issues in sentiment analysis is feature engineering while it severely affects time complexity and accuracy of sentiment classification. Design/methodology/approach In this paper, a feature reduction method is proposed that uses context-based knowledge as well as synset statistical knowledge. To do so, one-dimensional presentation proposed for SentiWordNet calculates statistical knowledge that involves polarity concentration and variation tendency for each synset. Feature reduction involves two phases. In the first phase, features that combine semantic and statistical similarity conditions are put in the same cluster. In the second phase, features are ranked and then the features which are given lower ranks are eliminated. The experiments are conducted by support vector machine (SVM), naive Bayes (NB), decision tree (DT) and k-nearest neighbors (KNN) algorithms to classify the vectors of the unigram and bigram features in two classes of positive or negative sentiments. Findings The results showed that the applied clustering algorithm reduces SentiWordNet synset to less than half which reduced the size of the feature vector by less than half. In addition, the accuracy of sentiment classification is improved by at least 1.5 per cent. Originality/value The presented feature reduction method is the first use of the synset clustering for feature reduction. In this paper features reduction algorithm, first aggregates the similar features into clusters then eliminates unsatisfactory cluster.

scholarly journals A weighted feature reduction method for power spectra of radar HRRPs

2006 ◽  
Vol 23 (3) ◽  
pp. 365-369 ◽  
Author(s):  
Lan Du ◽  
Hongwei Liu ◽  
Zheng Bao ◽  
Junying Zhang
Keyword(s):  
Reduction Method ◽  
Power Spectra ◽  
Feature Reduction

Frequency Analysis and Feature Reduction Method for Prediction of Cerebral Palsy in Young Infants

2016 ◽  
Vol 24 (11) ◽  
pp. 1225-1234 ◽  
Author(s):  
Hodjat Rahmati ◽  
Harald Martens ◽  
Ole Morten Aamo ◽  
Oyvind Stavdahl ◽  
Ragnhild Stoen ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Frequency Analysis ◽  
Reduction Method ◽  
Feature Reduction ◽  
Young Infants

scholarly journals Method of Feature Reduction in Short Text Classification Based on Feature Clustering

2019 ◽  
Vol 9 (8) ◽  
pp. 1578 ◽  
Author(s):  
Li ◽  
Yin ◽  
Shi ◽  
Mao ◽  
Shi
Keyword(s):  
Text Classification ◽  
Spectral Clustering ◽  
Reduction Method ◽  
Feature Reduction ◽  
Vector Spaces ◽  
Feature Clustering ◽  
Short Text ◽  
Cluster Feature ◽  
Original Feature ◽  
Traversal Algorithm

One decisive problem of short text classification is the serious dimensional disaster when utilizing a statistics-based approach to construct vector spaces. Here, a feature reduction method is proposed that is based on two-stage feature clustering (TSFC), which is applied to short text classification. Features are semi-loosely clustered by combining spectral clustering with a graph traversal algorithm. Next, intra-cluster feature screening rules are designed to remove outlier feature words, which improves the effect of similar feature clusters. We classify short texts with corresponding similar feature clusters instead of original feature words. Similar feature clusters replace feature words, and the dimension of vector space is significantly reduced. Several classifiers are utilized to evaluate the effectiveness of this method. The results show that the method largely resolves the dimensional disaster and it can significantly improve the accuracy of short text classification.

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