Feature Reduction with Inconsistency

2010 ◽  
Vol 4 (2) ◽  
pp. 77-87 ◽  
Author(s):  
Yong Liu ◽  
Yunliang Jiang ◽  
Jianhua Yang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Rough Set ◽  
Attribute Reduction ◽  
Classical Problem ◽  
Feature Reduction ◽  
Temporal Complexity ◽  
Semantic Correlation ◽  
Positive Region ◽  
Original Feature

Feature selection is a classical problem in machine learning, and how to design a method to select the features that can contain all the internal semantic correlation of the original feature set is a challenge. The authors present a general approach to select features via rough set based reduction, which can keep the selected features with the same semantic correlation as the original feature set. A new concept named inconsistency is proposed, which can be used to calculate the positive region easily and quickly with only linear temporal complexity. Some properties of inconsistency are also given, such as the monotonicity of inconsistency and so forth. The authors also propose three inconsistency based attribute reduction generation algorithms with different search policies. Finally, a “mini-saturation” bias is presented to choose the proper reduction for further predictive designing.

Feature Reduction with Inconsistency

2012 ◽  
pp. 195-204
Author(s):  
Yong Liu ◽  
Yunliang Jiang ◽  
Jianhua Yang
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Rough Set ◽  
Attribute Reduction ◽  
Classical Problem ◽  
Feature Reduction ◽  
Temporal Complexity ◽  
Semantic Correlation ◽  
Positive Region ◽  
Original Feature

Feature selection is a classical problem in machine learning, and how to design a method to select the features that can contain all the internal semantic correlation of the original feature set is a challenge. The authors present a general approach to select features via rough set based reduction, which can keep the selected features with the same semantic correlation as the original feature set. A new concept named inconsistency is proposed, which can be used to calculate the positive region easily and quickly with only linear temporal complexity. Some properties of inconsistency are also given, such as the monotonicity of inconsistency and so forth. The authors also propose three inconsistency based attribute reduction generation algorithms with different search policies. Finally, a “mini-saturation” bias is presented to choose the proper reduction for further predictive designing.

scholarly journals A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

2021 ◽  
Vol 22 (1) ◽  
pp. 53-66
Author(s):  
D. Anand Joseph Daniel ◽  
M. Janaki Meena
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
User Satisfaction ◽  
Performance Metrics ◽  
Computation Time ◽  
Feature Reduction ◽  
Training Data ◽  
Product Reviews ◽  
Online Product Reviews

Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.

A Method of Image Affective Semantic Classification Based on Attribution Reduction

2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Accuracy ◽  
Selection Process ◽  
Attribute Reduction ◽  
Image Features ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Semantic Classification ◽  
Low Level

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.

scholarly journals On the Interpretability of Machine Learning Models and Experimental Feature Selection in Case of Multicollinear Data

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 761
Author(s):  
Franc Drobnič ◽  
Andrej Kos ◽  
Matevž Pustišek
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forests ◽  
Experimental Approach ◽  
Feature Selection Method ◽  
Model Quality ◽  
Human In The Loop ◽  
Model Interpretation ◽  
Small Feature ◽  
Original Feature

In the field of machine learning, a considerable amount of research is involved in the interpretability of models and their decisions. The interpretability contradicts the model quality. Random Forests are among the best quality technologies of machine learning, but their operation is of “black box” character. Among the quantifiable approaches to the model interpretation, there are measures of association of predictors and response. In case of the Random Forests, this approach usually consists of calculating the model’s feature importances. Known methods, including the built-in one, are less suitable in settings with strong multicollinearity of features. Therefore, we propose an experimental approach to the feature selection task, a greedy forward feature selection method with least-trees-used criterion. It yields a set of most informative features that can be used in a machine learning (ML) training process with similar prediction quality as the original feature set. We verify the results of the proposed method on two known datasets, one with small feature multicollinearity and another with large feature multicollinearity. The proposed method also allows for a domain expert help with selecting among equally important features, which is known as the human-in-the-loop approach.

scholarly journals FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS

2012 ◽  
Vol 12 (04) ◽  
pp. 1240013 ◽  
Author(s):  
SAMANTA ROSATI ◽  
GABRIELLA BALESTRA ◽  
FILIPPO MOLINARI
Keyword(s):  
Feature Selection ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Rough Set ◽  
Near Infrared ◽  
Attribute Reduction ◽  
Diabetic Patients ◽  
Time Frequency ◽  
Before And After ◽  
Concentration Changes

Diabetic patients might present peripheral microcirculation impairment and might benefit from physical training. Thirty-nine diabetic patients underwent the monitoring of the tibialis anterior muscle oxygenation during a series of voluntary ankle flexo-extensions by near-infrared spectroscopy (NIRS). NIRS signals were acquired before and after training protocols. Sixteen control subjects were tested with the same protocol. Time-frequency distributions of the Cohen's class were used to process the NIRS signals relative to the concentration changes of oxygenated and reduced hemoglobin. A total of 24 variables were measured for each subject and the most discriminative were selected by using four feature selection algorithms: QuickReduct, Genetic Rough-Set Attribute Reduction, Ant Rough-Set Attribute Reduction, and traditional ANOVA. Artificial neural networks were used to validate the discriminative power of the selected features. Results showed that different algorithms extracted different sets of variables, but all the combinations were discriminative. The best classification accuracy was about 70%. The oxygenation variables were selected when comparing controls to diabetic patients or diabetic patients before and after training. This preliminary study showed the importance of feature selection techniques in NIRS assessment of diabetic peripheral vascular impairment.

scholarly journals Optimized Breast Cancer Classification using Feature Selection and Outliers Detection

2021 ◽  
pp. 298-307
Author(s):  
A. B Yusuf ◽  
R. M Dima ◽  
S. K Aina
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Breast Cancer Dataset ◽  
The Impact

Breast cancer is the second most commonly diagnosed cancer in women throughout the world. It is on the rise, especially in developing countries, where the majority of cases are discovered late. Breast cancer develops when cancerous tumors form on the surface of the breast cells. The absence of accurate prognostic models to assist physicians recognize symptoms early makes it difficult to develop a treatment plan that would help patients live longer. However, machine learning techniques have recently been used to improve the accuracy and speed of breast cancer diagnosis. If the accuracy is flawless, the model will be more efficient, and the solution to breast cancer diagnosis will be better. Nevertheless, the primary difficulty for systems developed to detect breast cancer using machine-learning models is attaining the greatest classification accuracy and picking the most predictive feature useful for increasing accuracy. As a result, breast cancer prognosis remains a difficulty in today's society. This research seeks to address a flaw in an existing technique that is unable to enhance classification of continuous-valued data, particularly its accuracy and the selection of optimal features for breast cancer prediction. In order to address these issues, this study examines the impact of outliers and feature reduction on the Wisconsin Diagnostic Breast Cancer Dataset, which was tested using seven different machine learning algorithms. The results show that Logistic Regression, Random Forest, and Adaboost classifiers achieved the greatest accuracy of 99.12%, on removal of outliers from the dataset. Also, this filtered dataset with feature selection, on the other hand, has the greatest accuracy of 100% and 99.12% with Random Forest and Gradient boost classifiers, respectively. When compared to other state-of-the-art approaches, the two suggested strategies outperformed the unfiltered data in terms of accuracy. The suggested architecture might be a useful tool for radiologists to reduce the number of false negatives and positives. As a result, the efficiency of breast cancer diagnosis analysis will be increased.

Vibration-Based Fault Diagnosis of Slurry Pumps Using the Neighborhood Rough Set Model

2009 ◽  
Author(s):  
Xiaomin Zhao ◽  
Qinghua Hu ◽  
Yaguo Lei ◽  
Ming J. Zuo
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Classification Accuracy ◽  
Flow Patterns ◽  
Neighborhood Size ◽  
Modified Model ◽  
Neighborhood Rough Set ◽  
Original Feature

Rough set has been widely used as a method of feature selection in fault diagnosis. The neighborhood rough set model can deal with both nominal and numerical features, but selecting the neighborhood size for its application may be a challenge. In this paper, we illustrate that using a single neighborhood size for all features may overestimate or underestimate a feature’s degree of dependency. The neighborhood rough set model is then modified by setting different neighborhood sizes for different features. The modified model is applied to fault diagnosis of slurry pump impellers. The chosen feature subsets generated by the modified rough set model can be physically explained by the corresponding flow patterns and generate higher classification accuracy than the original feature subsets and the feature subsets generated by the original rough set model.

scholarly journals A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hua Li ◽  
Deyu Li ◽  
Yanhui Zhai ◽  
Suge Wang ◽  
Jing Zhang
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Mathematical Tool ◽  
Knowledge Reduction ◽  
Variable Precision ◽  
Multilabel Learning ◽  
Decision Tables

Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable precision attribute reduct for multilabel data based on rough set theory, calledδ-confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and discernibility matrix associated withδ-confidence reduct are also introduced, from which we can obtain the approach to knowledge reduction in multilabel decision tables.

Social network analysis of law information privacy protection of cybersecurity based on rough set theory

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Li ◽  
Xuhua Hu
Keyword(s):  
Feature Selection ◽  
Social Network ◽  
Set Theory ◽  
Rough Set ◽  
Privacy Protection ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Practical Significance ◽  
Network Environment ◽  
Content Type

Purpose The purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply integrated into people’s daily life, especially under the interaction of the fierce development momentum of the Internet of Things and diversified personalized services, more and more private information of social users is exposed to the network environment actively or unintentionally. In addition, a large amount of social network data not only brings more benefits to network application providers, but also provides motivation for malicious attackers. Therefore, under the social network environment, the research on the privacy protection of user information has great theoretical and practical significance. Design/methodology/approach In this study, based on the social network analysis, combined with the attribute reduction idea of rough set theory, the generalized reduction concept based on multi-level rough set from the perspectives of positive region, information entropy and knowledge granularity of rough set theory were proposed. Furthermore, it was traversed on the basis of the hierarchical compatible granularity space of the original information system and the corresponding attribute values are coarsened. The selected test data sets were tested, and the experimental results were analyzed. Findings The results showed that the algorithm can guarantee the anonymity requirement of data publishing and improve the effect of classification modeling on anonymous data in social network environment. Research limitations/implications In the test and verification of privacy protection algorithm and privacy protection scheme, the efficiency of algorithm and scheme needs to be tested on a larger data scale. However, the data in this study are not enough. In the following research, more data will be used for testing and verification. Practical implications In the context of social network, the hierarchical structure of data is introduced into rough set theory as domain knowledge by referring to human granulation cognitive mechanism, and rough set modeling for complex hierarchical data is studied for hierarchical data of decision table. The theoretical research results are applied to hierarchical decision rule mining and k-anonymous privacy protection data mining research, which enriches the connotation of rough set theory and has important theoretical and practical significance for further promoting the application of this theory. In addition, combined the theory of secure multi-party computing and the theory of attribute reduction in rough set, a privacy protection feature selection algorithm for multi-source decision table is proposed, which solves the privacy protection problem of feature selection in distributed environment. It provides a set of effective rough set feature selection method for privacy protection classification mining in distributed environment, which has practical application value for promoting the development of privacy protection data mining. Originality/value In this study, the proposed algorithm and scheme can effectively protect the privacy of social network data, ensure the availability of social network graph structure and realize the need of both protection and sharing of user attributes and relational data.

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