Performance Evaluation of Feature Selection Methods on Large Dimensional Databases

Rani, Y. Leela Sandhya; Sucharita, V.; Bhattacharyya, Debnath; Kim, Hye-Jin

doi:10.14257/ijdta.2016.9.9.07

Cited by 1 publication

(1 citation statement)

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“…To distinguish which highlights might be applicable to a specific issue. [2] There are three algorithms featured in this paper they are: 1. Naïve Bayes Classifier 2.…”

Section: Feature Selectionmentioning

confidence: 99%

Human Activity Recognition using Smartphone

Rasekh¹,

Chen²,

Lü³

2019

IJRTE

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This paper consists of all the actions that are done by the people by using mobile phone. We used to record the actions by using the mobile phone. The main aim of this paper is to construct a classification model that is required for identifying actions of the people. This paper is used mainly to get solution of multi-classification problems. By using in a theoretic way, we can understand what the problem is, but we can only solve the problem by performing it in mathematical way. We can get very perfect result by solving in the mathematical way. Here we are deriving the actions of the persons using mobile phone, in a phone there are many sensors present in it. The sensors used in this paper are Accelerometer, Gyroscope. They are required for determining the actions of the person. The output obtained in this paper is used to compare the values in the term’s accuracy and precision. It uses a 3-dimension based accelerometer in order to collect the values obtained; there we determined that 31 values we contained in it. All the actions that are present are derived by using machine learning algorithms, they are, Naïve Bayes Classifiers, support vector machine, and neural networks. The output of the action determination by using the dataset required is used to determine a decrease of marking work to accomplish similar execution with machine learning.

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“…To distinguish which highlights might be applicable to a specific issue. [2] There are three algorithms featured in this paper they are: 1. Naïve Bayes Classifier 2.…”

Section: Feature Selectionmentioning

confidence: 99%