English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In Machine Learning K-nearest Neighbor is a renowned supervised learning method. The traditional KNN has the unlike requirement of specifying ‘K’ value in advance for all test samples. The earlier solutions of predicting ‘K’ values are mainly focused on finding optimal-k-values for all samples. The time complexity to obtain the optimal-k-values in the previous method is too high. In this paper, a Modified K-Nearest Neighbor algorithm with Variant K is proposed. The KNN algorithm is divided in the training and testing phase to find K value for every test sample. To get the optimal K value the data is trained for various K values with Min-Heap data structure of 2*K size. K values are decided based on the percentage of training data considered from every class. The Indian Classical Music is considered as a case study to classify it in different Ragas. The Pitch Class Distribution features are input to the proposed algorithm. It is observed that the use of Min-Heap has reduced the space complexity nonetheless Accuracy and F1-score for the proposed method are increased than traditional KNN algorithm as well as Support Vector Machine, Decision Tree Classifier for Self-Generated Dataset and Comp-Music Dataset.

Modified K-nearest Neighbor Algorithm with Variant K Values