Feature Selection for Stock Market Analysis

He, Yuqinq; Fataliyev, Kamaladdin; Wang, Lipo

doi:10.1007/978-3-642-42042-9_91

Cited by 29 publications

(21 citation statements)

References 14 publications

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“…For example, Pali and Bhaiya [35] used a GA for feature selection in face database with the use of neural network classifier. Another example is He et al [36] that applied a genetic algorithm for feature selection in the field of financial market to find out the most significant factors to the stock market that is very important in this field. As shown from these examples, testing these genetic algorithms was using specific type of datasets that makes testing is not sufficient and make comparisons with them is difficult.…”

Section: ) Research Issuesmentioning

confidence: 99%

A Review on Evolutionary Feature Selection

Abd-Alsabour

2014

2014 European Modelling Symposium

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This paper presents a review of some of the most recent evolutionary algorithms used for solving feature selection based upon previously published research on feature selection. In addition, we discuss various research issues relating to each of the presented evolutionary algorithm. Evolutionary algorithms present several advantages over traditional search such as they require less domain-specific information. Such advantages have made them very popular within feature selection as explained in this paper. This paper covers the first part only of the evolutionary algorithms for the feature selection problem due to the limitation of the number of pages. The references cited in this paper cover the major theoretical issues, and provide access to the main branches of the literature dealing with such methods.

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Section: ) Research Issuesmentioning

confidence: 99%

A Review on Evolutionary Feature Selection

Abd-Alsabour

2014

2014 European Modelling Symposium

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“…However, creating a classification model with from a dataset with high-dimensionality is time consuming and may converge to a local minima given the large search space. Therefore, selecting a reduced set of relevant features in an audio sample can improve immensely the performance generating a classification model ( 48 ). There are many techniques for feature selection including Shannon Entropy (SH), Fisher score, Mel-Frequency Cepstral Coefficients, and Zero Crossing Rate (ZCR).…”

Section: The Proposed Systemmentioning

confidence: 99%

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework

et al. 2021

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Respiratory symptoms can be caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms: coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases, such as the COVID-19 pandemic. Among the factors that contributed to the spread of the COVID-19 pandemic were the late diagnosis or misinterpretation of COVID-19 symptoms as regular flu-like symptoms. Research has shown that one of the possible differentiators of the underlying causes of different respiratory diseases could be the cough sound, which comes in different types and forms. A reliable lab-free tool for early and accurate diagnosis, which can differentiate between different respiratory diseases is therefore very much needed, particularly during the current pandemic. This concept paper discusses a medical hypothesis of an end-to-end portable system that can record data from patients with symptoms, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly theoretical solution could play an important part in the early diagnosis.

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“…Rao, R. V. et al [10] introduced adaptive teaching factor by modifying the teaching factor of TLBO by assigning more than one teacher for the learners. He, Y. et al [11] analyzed the performance of feature selection methodology of PCA and Sequential Forward Selection (SFS) with SVR and concluded that PCA perform better accuracy than SFS. Hsu, C.M.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Stock Index of Tata Steel using Hybrid Machine Learning Based Optimization Techniques

Siddique¹,

Panda²

2019

IJRTE

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The trend of stock price prediction has always been in the focal point of analytical activity in financial domain for both the researchers and investors. Prediction with accuracy is very essential for improved investment decisions that imbibe minimum risk factors. Due to this, majority of investors depend upon that intelligent trading system which generates better forecasting results. As forecasting stock market price with high accuracy is quite a challenging task for the analysts, machine learning has been adopted as one of the popular techniques to predict future trends. Even if there are many recognized analytical time series analysis that are categorized either under soft computing or under conventional statistical techniques like fuzzy logic, artificial neural networks and genetic algorithms, researchers have been looking for more appropriate techniques which can exhibit improved results. In this paper, we developed different hybrid machine learning based prediction models and compared their efficiency. Dimension reduction techniques such as orthogonal forward selection (OFS) and kernel principal component analysis (KPCA) are used separately with support vector regression (SVR) and teaching learning based optimization (TLBO) to predict the stock price of Tata Steel. The performance of both the proposed approach is evaluated with 4143days daily transactional data of Tata steels stocks price, which was collected from Bombay Stock Exchange (BSE). We compared the results of both OFS-SVR-TLBO and KPCA-SVR-TLBO hybrid models and concludes that by incorporating KPCA is more practicable and performs better results than OFS

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Feature Selection for Stock Market Analysis

Cited by 29 publications

References 14 publications

A Review on Evolutionary Feature Selection

A Review on Evolutionary Feature Selection

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework

Prediction of Stock Index of Tata Steel using Hybrid Machine Learning Based Optimization Techniques

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