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(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 10, 2020Modified Knearest Neighbor Algorithm with Variant K Values Kalyani C. Waghmare1, Balwant A. Sonkamble2 Department of Computer Engineering Pune Institute of Computer Technology, Pune, IndiaAbstractIn Machine Learning Knearest 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

How to fill out modified k-nearest neighbor algorithm

How to fill out modified k-nearest neighbor algorithm

1. Gather your dataset that contains features and labels.
2. Preprocess the data by normalizing or standardizing the feature values.
3. Choose an appropriate number of neighbors (k) for the algorithm.
4. For each data point to classify, calculate the distance to every point in the dataset using a distance metric (e.g., Euclidean, Manhattan).
5. Identify the 'k' closest points based on the calculated distances.
6. Apply the modified algorithm adjustments (if applicable) to consider weights or specific parameters for the neighbors.
7. Determine the most common class label among the 'k' neighbors or apply a weighted voting scheme.
8. Assign the predicted label to the data point.

Who needs modified k-nearest neighbor algorithm?

1. Data scientists and machine learning engineers working on classification tasks.
2. Researchers conducting studies in pattern recognition and data mining.
3. Businesses looking to implement recommendation systems based on user preferences.
4. Developers creating applications that require pattern matching in various domains.

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What is modified k-nearest neighbor algorithm?

The modified k-nearest neighbor algorithm is a variation of the traditional k-nearest neighbor algorithm used in machine learning and data analysis, which adjusts the standard approach to improve accuracy or efficiency in specific applications. It often includes enhancements such as weighting the contributions of neighbors or incorporating distance metrics.

Who is required to file modified k-nearest neighbor algorithm?

The modified k-nearest neighbor algorithm is typically utilized by data scientists, machine learning practitioners, or analysts who need to classify or predict outcomes based on input data. It is not a filing requirement but rather a methodological choice in data processing.

How to fill out modified k-nearest neighbor algorithm?

To implement the modified k-nearest neighbor algorithm, one must first determine the dataset to use, select the value of 'k', preprocess the data for normalization, choose a distance metric, and then apply the algorithm to identify the nearest neighbors based on the given parameters. Coding in programming languages like Python or R often involves utilizing libraries that support this algorithm.

What is the purpose of modified k-nearest neighbor algorithm?

The purpose of modified k-nearest neighbor algorithm is to improve the accuracy and performance of classification or regression tasks by refining the selection and influence of neighboring data points according to specific criteria, such as proximity or relevancy.

What information must be reported on modified k-nearest neighbor algorithm?

When reporting on the modified k-nearest neighbor algorithm, it's important to include details such as the dataset used, the value of 'k', the distance metric chosen, results of accuracy or performance metrics, and any preprocessing steps taken, along with conclusions drawn from the analysis.