Logistic regression modeling for context-based classification

Brzezinski, Jacek; Knafl, George J.

doi:10.1109/dexa.1999.795279

Cited by 37 publications

(13 citation statements)

References 4 publications

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“…It is a type of linear regression that is used for predicting binary or multiclass dependent variables [62]. Logistic Regression (LR) [63] is a classical classification method that has been used widely in many applications including document classification [66,67], computer vision [68], natural language processing [69], and bioinformatics [70, 71, & 72]. Logistic regression can be defined mathematically as Pr (G = k | X = x) is a nonlinear function of x and range from 0 to 1 & sum up to 1.…”

Section: Tree Classifiersmentioning

confidence: 99%

Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical and support vector machine classifiers

Yadav

Kumar

Sahoo

2012

2012 National Conference on Computing and Communication Systems

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The prediction of Parkinson's disease in early age has been challenging task among researchers because the symptoms of disease come into existence in middle and late middle age. There is lot of the symptoms that leads to Parkinson's disease. But this paper focus on the speech articulation difficulty symptoms of PD affected people and try to formulate the model on the behalf of three data mining methods. These three data mining methods are taken from three different domains of data mining i.e. from tree classifier, statistical classifier and support vector machine classifier. Performance of these three classifiers is measured with three performance matrices i.e. accuracy, sensitivity and specificity. So, the main task of this paper is tried to find out which model identified the PD affected people more accurately.

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Section: Tree Classifiersmentioning

confidence: 99%

Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical and support vector machine classifiers

Yadav

Kumar

Sahoo

2012

2012 National Conference on Computing and Communication Systems

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“…We compare the results from our model against those of other existing action models including GMM [8], HMM [12,1], logistic regression (LR) [5], SVM [6] and CRF [20].…”

Section: Methodsmentioning

confidence: 99%

Action categorization with modified hidden conditional random field

Zhang

Gong

2010

Pattern Recognition

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In this paper, we present a method for action categorization with a modified hidden conditional random field (HCRF). Specifically, effective silhouette-based action features are extracted using motion moments and spectrum of chain code. We formulate a modified HCRF (mHCRF) to have a guaranteed global optimum in the modelling of the temporal action dependencies after the HMM pathing stage. Experimental results on action categorization using this model are compared favorably against several existing modelbased methods including GMM, SVM, Logistic Regression, HMM, CRF and HCRF.

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“…[23], [24], [25] fits our application, since it is a dichotomous classification problem with multiple predictor variables, where the predictor variables are the terms of our "vocabulary". Classical classification methods such as Logistic Regression which has applications in a wide variety of domains can also be used for document classification [26]. Thus, Logistic Regression can also be applied for our problem.…”

Section: Location Index Computationmentioning

confidence: 99%

Spatio-temporal Signal Recovery from Political Tweets in Indonesia

Mazumder

Das

Kim

et al. 2013

2013 International Conference on Social Computing

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Abstract-Online social network community now provides an enormous volume of data for analyzing human sentiment about people, places, events and political activities. It is increasingly clear that analysis of such data can provide great insights on the social, political and cultural aspect of the participants of these networks. As part of the Minerva project, currently underway at Arizona State University, we have analyzed a large volume of Twitter data to understand radical political activity in the provinces of Indonesia. Based on analysis of radical/counter radical sentiments expressed in tweets by Twitter users, we create a Heat Map of Indonesia which visually demonstrates the degree of radical activities in various provinces of Indonesia. We create the Heat Map of Indonesia by computing (i) the Radicalization Index and (ii) the Location Index of each Twitter user from Indonesia, who has expressed some radical sentiment in her tweets. The conclusions derived from our analysis matches significantly with the analysis of Wahid Institute, a leading political think tank of Indonesia, thus validating our results.

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Logistic regression modeling for context-based classification

Cited by 37 publications

References 4 publications

Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical and support vector machine classifiers

Predication of Parkinson's disease using data mining methods: A comparative analysis of tree, statistical and support vector machine classifiers

Action categorization with modified hidden conditional random field

Spatio-temporal Signal Recovery from Political Tweets in Indonesia

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