scholarly journals WeightedLD: The Application of Sequence Weights to Linkage Disequilibrium

2021 ◽  
Author(s):  
Oscar J Charles ◽  
Joeseph Roberts ◽  
Judith Breuer ◽  
Richard A Goldstein

Sequence-weighting methods are commonly employed to account for biases in sequence datasets. We use a weighting scheme which considers the observed distinctiveness of sequences and apply it to calculations of linkage disequilibrium. Each sequence now contributes a weighted score to linkage disequilibrium measurements of pairwise loci. We demonstrate that this reduces the effect of uneven sampling, as underrepresented groups of sequences will each contribute more individually than redundant, similar sequences.

Term Weighting Scheme (TWS) is a key component of the matching mechanism when using the vector space model In the context of information retrieval (IR) from text documents, the this paper described a new approach of term weighting methods to improve the classification performance. In this study, we propose an effective term weighting scheme, which gives highest accuracy with compare to the text classification methods. We compared performance parameter of KNN and Naïve Bayes Classification with different Weighting Method, Weight information gain, SVM and proposed method.We have implemented many term-weighting methods (TWM) on Amazon data collections in combination with Information-Gain and SVM and KNN algorithm and Naïve Bayes Algorithm.


2003 ◽  
Vol 110 (2) ◽  
pp. 396-396
Author(s):  
No authorship indicated

2003 ◽  
Vol 15 (2) ◽  
pp. 136-136
Author(s):  
No authorship indicated

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