A Lexical Approach for Text Categorization of Medical Documents

Jindal, Rajni; Taneja, Shweta

doi:10.1016/j.procs.2015.02.026

Cited by 15 publications

(12 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then the average comparison results show that the performance enhancement of the DPCA-MGAC technique. As a result of the comparison, the precision is increased by 6% and 12% when compared to existing GSRM and LKNN respectively [9], [10].…”

Section: Figure 2 Performance Results Of Precisionmentioning

confidence: 97%

“…The results and discussion of proposed DPCA-MGAC technique and existing methods are described with various performance metrics such as precision, false positive rate and time complexity. With the help of these parametric analysis, the comparison between three methods namely DPCA-MGAC technique, GSRM and LKNN is performed [9], [10]. The comparison results of three methods are explained in following section.…”

Section: Resultsmentioning

confidence: 99%

“…2 illustrates the performance results of precision with respect to a number of documents where the numbers of documents related to diabetes are taken in "x" direction and the performance results of precision are shown in "y" direction. As a result, the two-dimensional graphical views of the precision with three different methods DPCA-MGAC technique, and existing GSRM and LKNN are clearly illustrated [9], [10]. It clearly depicts that the precision is considerably increased using DPCA-MGAC technique than the other two existing methods.…”

Section: A Impact Of Precisionmentioning

confidence: 86%

“…An experimental evaluation of the proposed DPCA-MGAC technique and existing methods namely Graphbased Semantic Ranking Model (GSRM) and LKNN (Lexical KNN) algorithms are implemented using MATLAB and PubMed database as input [9], [10].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…A novel lexical approach based on k-nearest neighbor (LKNN) algorithm was designed for classifying the text documents in the biomedical domain [10]. But LKNN has more time complexity since it failed to use any feature selection.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Discriminant Pearson Correlative Feature Selection based Gentle Adaboost Classification for Medical Document Mining

2019

IJRTE

View full text Add to dashboard Cite

This paper examines Discriminant Pearson Correlative Analysis Based Multivariate Gentle Adaboost Classification (DPCA-MGAC) and it is used to improve the performance of medical document mining with minimum time complexity. A large number of documents are collected from PubMed databases through the semantic-based search. Processes such as removing stop words, stemming, features identification, selection of features i.e., relevant keywords for document classification are carried out. The significant feature selection is carried out using DPCA, and with the selected features the documents are categorized into different classes using MGAC. This classification process combines the results of all weak learners and makes a strong classification in order to improve the precision of medical data mining and minimizes the false positive rate. Experimental evaluation has been performed using PubMed database.

show abstract

Section: Figure 2 Performance Results Of Precisionmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: A Impact Of Precisionmentioning

confidence: 86%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Discriminant Pearson Correlative Feature Selection based Gentle Adaboost Classification for Medical Document Mining

2019

IJRTE

View full text Add to dashboard Cite

show abstract

Taxonomy grooming algorithm ‐ An autodidactic domain specific dimensionality reduction approach for fast clustering of social media text data

Renjith

Sreekumar

Jathavedan

2022

Concurrency and Computation

View full text Add to dashboard Cite

Social media being the most eminent source toward the growth of big data is important for information retrieval‐based applications to improve the efficiency in proportional to the volume it must deal with. One way to achieve better performance is to upgrade the processing capacity and the alternative option is to improve the processing methodology. The latter can be achieved using smarter processing techniques and/or better algorithms. Reducing the data volume that needs to be processed is a good strategy and it can be achieved by extracting only the relevant information via user segmentation by adopting an appropriate clustering technique. However, while dealing with text content, clustering algorithms do suffer due to the very high dimensions to be dealt with. Since the domain‐specific aspects are getting lost while applying traditional dimensionality reduction approaches, it is important to device an alternate strategy. This work proposes a taxonomy grooming algorithm (TGA), an autodidactic domain‐specific dimensionality reduction approach, for fast clustering of social media text data. Our experiment results are very promising and the dimensionality reduction using TGA resulted in better results in comparison with the traditional dimensionality reduction approaches.

show abstract