Linear regression for pattern recognition

Stephen, Priya; Jaganathan, Suresh

doi:10.1109/icgccee.2014.6921393

Cited by 13 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Situations also arise where the beneficiaries of the insurance end up harming the policy holder, to get the money quickly. In this paper we are proposing to use, linear regression analysis [4] , to predict the patient charges and forecast the insurance values of patients.…”

Section: Discussionmentioning

confidence: 99%

“…The insurance companies rely on these algorithms to predict the correct amount that they can set as insurance coverage, so that they can make the most profit. There is already a lot of related work in this field, but this paper focuses on the linear regression [4] analysis, and proposes to improve the existing work and make a more efficient analysis.…”

Section: Fig 1: Sqlmentioning

confidence: 99%

“…All the different types of analyses have some sort of limitations and assumptions made for them to work. Linear Regression [4] has few of them as well. They include homoscedascity [5] , multicollinearity [6] , goodness of fit [7] , normality of dataset [8] , etc.…”

Section: Contributionmentioning

confidence: 99%

“…These attributes tell information about the patient, if he/she has children, smokes, their age, gender, BMI, etc. We will be using Linear Regression [4] analysis to analyse the available dataset and forecast the patient charges for the insurance company. The shortcomings of linear regression [4] like multicollinearity [6] , homoscedascity [5] , normality and other assumptions will be first taken care of by doing data cleaning on the dataset, and then the analyses will be done.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Forecasting Insurance and Patient Charges using Linear Regression

Natarajan,

Das,

Patil

et al. 2020

IJITEE

View full text Add to dashboard Cite

The insurance companies around the world work with very simple formula and have a very specific agenda. They convince people to deposit money on their name to the insurance company, in return the people are promised to be given a large sum of amount when they get an expensive hospital bill or when they meet with an accident. This amount to be paid, is generally taken from people on a monthly basis. Customers are convinced to join such a scheme as it is very tempting and the prospect of money troubles taken care of for nothing in a time of crisis seems wonderful. Insurance companies on the other hand pray that nothing happens to the customers or their families, so that they don't come looking for compensation. The money that they collect from new insurance holders is what they use to pay of the losses. Data analysis is the process of understanding the behaviour of a certain dataset when measured against certain static quantities. In this paper we are proposing to use Data science and in particular regression analysis, to analyse a dataset of patients and devise a method to predict their insurance amount. There are various types of learning and broadly speaking linear regression comes under supervised learning. We have a dataset consisting of over 1300 patients each with 7 characteristics like smoker or not, do they have children, their age, sex, BMI, etc. We are also proposing to devise methods to overcome the shortcomings of Linear regression like multicollinearity and homoscedascity, and perform the required data cleaning..

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Fig 1: Sqlmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting Insurance and Patient Charges using Linear Regression

Natarajan,

Das,

Patil

et al. 2020

IJITEE

View full text Add to dashboard Cite

show abstract

“…In [10], Naseem et al proposed a linear regression classifier (LRC) that a query image is linearly represented by the training samples of each subject. In [11], linear regression‐based classification is developed to conduct other applications of pattern recognition such as handwriting recognition and digits recognition. These methods use the training samples of one category to represent a query image while ignoring the effect of other subjects in computing the nearest subspace.…”

Section: Introductionmentioning

confidence: 99%

Multi‐step linear representation‐based classification for face recognition

Liu

2016

IET Computer Vision

View full text Add to dashboard Cite

Error detection is an important approach to improve the robustness of face recognition method. However, it is hard to directly detect the invalid pixels in a facial image. The authors decompose the hard problem into many simpler sub‐problems in this study. That is, the error detection process of pixels is divided into multiple phases and a portion of invalid pixels are detected in each phase. The goal is to decrease the ratio of invalid pixels to the whole pixels in a testing image, which progressively improves the final recognition accuracy. The performance that their method deals with occlusion and corruption problems is evaluated on different databases. In addition, the comparison with other state‐of‐the‐art studies shows that the proposed method achieves the best results in face occlusion and disguise issues.

show abstract