Review on Feature Selection and Classification using Neuro-Fuzzy Approaches

2016 ◽  
Vol 7 (4) ◽  
pp. 28-44 ◽  
Author(s):  
Saroj Biswas ◽  
Monali Bordoloi ◽  
Biswajit Purkayastha
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Computation Time ◽  
Recent Survey ◽  
Learning Problems ◽  
Fuzzy Approach ◽  
Redundant Data ◽  
Classification Feature ◽  
Research Article ◽  
Neuro Fuzzy

This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and for providing some insight to the user about the symbolic knowledge embedded within the network. The neuro–fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied on standard datasets to demonstrate the applicability of neuro-fuzzy approaches.

Recent Neuro-Fuzzy Approaches for Feature Selection and Classification

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Heisnam Rohen Singh ◽  
Saroj Kr Biswas ◽  
Monali Bordoloi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Computation Time ◽  
Prediction Performance ◽  
Learning Problems ◽  
Fuzzy Approach ◽  
Symbolic Forms ◽  
Redundant Data ◽  
Neuro Fuzzy ◽  
And Performance

Classification is the task of assigning objects to one of several predefined categories. However, developing a classification system is mostly hampered by the size of data. With the increase in the dimension of data, the chance of irrelevant, redundant, and noisy features or attributes also increases. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy, and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and classification with better insight by representing knowledge in symbolic forms. The neuro-fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied to standard datasets to demonstrate their applicability and performance.

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.

scholarly journals Unsupervised feature selection using a neuro-fuzzy approach

1998 ◽  
Vol 19 (11) ◽  
pp. 997-1006 ◽  
Author(s):  
Jayanta Basak ◽  
Rajat K. De ◽  
Sankar K. Pal
Keyword(s):  
Feature Selection ◽  
Fuzzy Approach ◽  
Unsupervised Feature Selection ◽  
Neuro Fuzzy

scholarly journals Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition

2021 ◽  
Vol 9 (1) ◽  
pp. 60-74
Author(s):  
Derry Pramono Adi ◽  
Lukman Junaedi ◽  
Frismanda ◽  
Agustinus Bimo Gumelar ◽  
Andreas Agung Kristanto
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Customer Service ◽  
Harmony Search ◽  
Computation Time ◽  
Principal Component ◽  
Support Vector ◽  
Bee Colony ◽  
Speech Data ◽  
Selection Algorithms

Initially, the goal of Machine Learning (ML) advancements is faster computation time and lower computation resources, while the curse of dimensionality burdens both computation time and resource. This paper describes the benefits of the Feature Selection Algorithms (FSA) for speech data under workload stress. FSA contributes to reducing both data dimension and computation time and simultaneously retains the speech information. We chose to use the robust Evolutionary Algorithm, Harmony Search, Principal Component Analysis, Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Bee Colony Optimization, which are then to be evaluated using the hierarchical machine learning models. These FSAs are explored with the conversational workload stress data of a Customer Service hotline, which has daily complaints that trigger stress in speaking. Furthermore, we employed precisely 223 acoustic-based features. Using Random Forest, our evaluation result showed computation time had improved 3.6 faster than the original 223 features employed. Evaluation using Support Vector Machine beat the record with 0.001 seconds of computation time.

scholarly journals A New Intelligent Approach for Effective Recognition of Diabetes in the IoT E-HealthCare Environment

2020 ◽  
Author(s):  
Amin Ul Haq ◽  
Jianping Li ◽  
Jalaluddin khan ◽  
Muhammad Hammad Memon ◽  
Shah Nazir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Decision Tree ◽  
High Performance ◽  
Computation Time ◽  
Healthcare Services ◽  
Experimental Results ◽  
Filter Method ◽  
Diagnosis System ◽  
Healthcare Environment

A significant attention has been made to the accurate detection of diabetes which is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the IoT e-healthcare environment. Internet of Things (IOT) has emerging role in healthcare services which delivers a system to analyze the medical data for diagnosis of diseases applied data mining methods. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a IOT based diagnosis system using machine learning methods, such as preprocessing of data, feature selection, and classification for the detection of diabetes disease in e- healthcare environment. Model validation and performance evaluation metrics have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning Decision Tree algorithms, such as Ada Boost and Random Forest are also used for feature selection and compared the classifier performance with wrapper based feature selection algorithms also. Machine learning classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the Decision Tree algorithm based on selected features improves the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high as compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and GL, DPF, and BMI are more significantly important features in the dataset for prediction of diabetes disease. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would be effectively detected diabetes disease and can easily be deployed in IOT wireless sensor technologies based e-healthcare environment.

Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions

2021 ◽  
Vol 17 (3) ◽  
pp. 63-79
Author(s):  
Alti Adel ◽  
Ayeche Farid
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Recognition Rate ◽  
Classification Problem ◽  
Expression Recognition ◽  
Emotion Classification ◽  
Feature Classification ◽  
Shape Model ◽  
Classification Feature ◽  
Human Emotions

Facial expression recognition is a human emotion classification problem attracting much attention from scientific research. Classifying human emotions can be a challenging task for machines. However, more accurate results and less execution time are still the issues when extracting features of human emotions. To cope with these challenges, the authors propose an automatic system that provides users with a well-adopted classifier for recognizing facial expressions in a more accurate manner. The system is based on two fundamental machine learning stages, namely feature selection and feature classification. Feature selection is realized by active shape model (ASM) composed of landmarks while the feature classification algorithm is based on seven well-known classifiers. The authors have used CK+ dataset, implemented and tested seven classifiers to find the best classifier. The experimental results show that quadratic classifier (DA) provides excellent performance, and it outperforms the other classifiers with the highest recognition rate of 100% for the same dataset.

Advanced Dimensionality Reduction Method for Big Data

Big Data ◽  
2016 ◽  
pp. 2388-2400 ◽  
Author(s):  
Sufal Das ◽  
Hemanta Kumar Kalita
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Big Data ◽  
Dimensionality Reduction ◽  
Association Rule ◽  
Computation Time ◽  
Distribution Management ◽  
Redundant Data ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The growing glut of data in the worlds of science, business and government create an urgent need for consideration of big data. Big data is a term that describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information. Big data challenge is becoming one of the most exciting opportunities for the next years. Data mining algorithms like association rule mining perform an exhaustive search to find all rules satisfying some constraints. it is clear that it is difficult to identify the most effective rule from big data. A novel method for feature selection and extraction has been introduced for big data using genetic algorithm. Dimensionality reduction can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, to obtain the accuracy and saves the computation time and simplifies the result. A genetic algorithm was developed based approach utilizing a feedback linkage between feature selection and association rule using MapReduce for big data.

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