k nearest neighbor matlab

For classification and regression issues, the k-Nearest Neighbors (k-NN) algorithm is examined as a basic and efficient technique. Encompassing Statistics and Machine Learning Toolbox, MATLAB offers different functions and tools to apply k-NN. We recommend a gradual instruction to apply k-NN in MATLAB for classification as well as regression:

Step 1: Load the Data

Initially, it is advisable to load or create the dataset. We plan to employ the in-built Fisher’s Iris dataset in this instance.

% Load the Fisher’s Iris dataset

load fisheriris

% Features and labels

X = meas; % Features

Y = species; % Labels

Step 2: Split the Data

The data must be divided into testing and training sets.

% Split data into training and testing sets

cv = cvpartition(Y, ‘HoldOut’, 0.3); % 30% data for testing

X_train = X(training(cv), :);

Y_train = Y(training(cv), :);

X_test = X(test(cv), :);

Y_test = Y(test(cv), :);

Step 3: Implement k-NN Classifier

To develop a k-NN classifier, our team focuses on employing the fitcknn function. For categorizing the test data, it is beneficial to utilize the predict function.

% Create a k-NN classifier

k = 5; % Number of neighbors

knnModel = fitcknn(X_train, Y_train, ‘NumNeighbors’, k);

% Predict the labels of the test data

Y_pred = predict(knnModel, X_test);

% Evaluate the classifier

confMat = confusionmat(Y_test, Y_pred);

disp(‘Confusion Matrix:’);

disp(confMat);

accuracy = sum(diag(confMat)) / sum(confMat(:));

disp([‘Accuracy: ‘, num2str(accuracy * 100), ‘%’]);

Step 4: k-NN for Regression

As an alternative, we can employ the fitrknn function when we are dealing with a regression issue. The following is an instance employing synthetic data.

% Generate synthetic data for regression

X = rand(100, 1) * 10; % Features

Y = 2 * X + randn(100, 1); % Labels with noise

% Split the data into training and testing sets

cv = cvpartition(size(X, 1), ‘HoldOut’, 0.3);

X_train = X(training(cv), :);

Y_train = Y(training(cv), :);

X_test = X(test(cv), :);

Y_test = Y(test(cv), :);

% Create a k-NN regression model

k = 5; % Number of neighbors

knnModel = fitrknn(X_train, Y_train, ‘NumNeighbors’, k);

% Predict the values of the test data

Y_pred = predict(knnModel, X_test);

% Evaluate the regression model

mse = mean((Y_test – Y_pred).^2);

disp([‘Mean Squared Error: ‘, num2str(mse)]);

Step 5: Visualize the Results

Our team intends to visualize the regression outcomes with an aid of scatter plot or the classification outcomes through the utilization of a confusion matrix.

% Classification results visualization

figure;

confusionchart(Y_test, Y_pred);

title(‘Confusion Matrix for k-NN Classification’);

% Regression results visualization

figure;

scatter(X_test, Y_test, ‘filled’);

hold on;

scatter(X_test, Y_pred, ‘filled’);

plot(X_test, Y_pred, ‘r’);

xlabel(‘X’);

ylabel(‘Y’);

legend(‘Actual’, ‘Predicted’, ‘Location’, ‘best’);

title(‘k-NN Regression Results’);

hold off;

Supplementary Customizations

Through altering hyperparameters like the distance weight, distance metric, and others, we could further adapt the k-NN method.

% Customizing the k-NN classifier

knnModel = fitcknn(X_train, Y_train, …

‘NumNeighbors’, k, …

‘Distance’, ‘euclidean’, … % Other options: ‘cityblock’, ‘chebychev’, ‘minkowski’

‘DistanceWeight’, ‘inverse’, … % Other options: ‘equal’, ‘squaredinverse’

‘Standardize’, true); % Standardize the data

% Customizing the k-NN regression model

knnModel = fitrknn(X_train, Y_train, …

‘NumNeighbors’, k, …

‘Distance’, ‘euclidean’, …

‘DistanceWeight’, ‘inverse’, …

‘Standardize’, true);

k nearest neighbor matlab projects

There exist several project ideas based on k-Nearest Neighbor (k-NN). A broad scope of applications, from simple algorithm deployment to innovative machine learning missions are encompassed in 50 k-NN projects in MATLAB. We offer 50 project plans with short explanations:

Basic k-NN Projects

1. Simple k-NN Classifier

• For the Iris dataset, we plan to apply a simple k-NN classifier.

2. k-NN Classifier with Custom Distance Metric

• Through the utilization of a conventional distance metric, it is appreciable to utilize a k-NN classifier.

3. k-NN Classifier with Weighted Distance

• With the aid of distance weighting, we have to execute a K-NN classifier.

4. k-NN Regression

• Mainly, for a basic regression issue, our team focuses on applying k-NN.

5. k-NN with Cross-Validation

• For model assessment, it is beneficial to utilize k-NN with cross-validation.

Image Processing and Computer Vision

1. Handwritten Digit Classification

• From the MNIST dataset, categorize handwritten digits through the utilization of the k-NN algorithm.

2. Image Classification with k-NN

• For categorizing CIFAR-10 images, we aim to apply k-NN.

3. Face Recognition

• Generally, the k-NN method should be employed for missions of face recognition.

4. Object Detection

• For simple object detection, our team plans to utilize k-NN.

5. Image Segmentation

• To divide images into various areas, we focus on employing k-NN.

Signal Processing

1. ECG Signal Classification

• With the aid of k-NN, it is appreciable to categorize ECG signals.

2. Speech Recognition

• For simple speech recognition missions, our team aims to apply the k-NN algorithm.

3. Audio Genre Classification

• By means of employing k-NN, we plan to categorize audio files into genres.

4. Noise Reduction in Signals

• In signal processing, it is significant to utilize the k-NN method for noise mitigation.

5. Time-Series Forecasting

• As a means to predict time-series data, our team intends to implement k-NN.

Natural Language Processing

1. Text Classification

• For text classification missions, it is appreciable to utilize k-NN.

2. Spam Email Detection

• To categorize emails as junk or legitimate, we focus on employing k-NN.

3. Sentiment Analysis

• Through the utilization of the k-NN algorithm, our team plans to carry out sentiment analysis on text data.

4. Named Entity Recognition

• Mainly, for named entity recognition in text, it is approachable to employ k-NN.

5. Language Detection

• For identifying the language of a text, we intend to execute k-NN.

Biomedical Engineering

1. Medical Diagnosis

• From medical data, identify illnesses with the support of k-NN.

2. Gene Expression Classification

• By means of employing the k-NN method, our team focuses on categorizing gene expression data.

3. Brain-Computer Interface

• In brain-computer interfaces, categorize EEG signals through applying k-NN in an effective manner.

4. Drug Response Prediction

• Through the utilization of k-NN, we plan to forecast drug reactions.

5. Patient Risk Stratification

• On the basis of medical logs, classify risk of patients by means of employing the k-NN algorithm.

Financial Engineering

1. Stock Price Prediction

• In order to forecast stock prices, it is beneficial to utilize k-NN.

2. Credit Scoring

• For credit scoring systems, our team aims to execute k-NN.

3. Fraud Detection

• With the aid of the k-NN method, we focus on identifying fraud transactions.

4. Portfolio Optimization

• As a means to reinforce investment portfolios, it is appreciable to employ k-NN.

5. Market Segmentation

• Through the utilization of k-NN, financial markets must be divided.

Robotics

1. Robot Path Planning

• For path planning in robotics, we aim to utilize the k-NN algorithm.

2. Obstacle Avoidance

• In robots, k-NN has to be employed for obstacle prevention.

3. Gesture Recognition

• Regarding robotic management, make use of k-NN to interpret the movements.

4. SLAM (Simultaneous Localization and Mapping)

• In SLAM, our team focuses on employing k-NN for the process of feature matching.

5. Autonomous Driving

• Generally, in automated driving, the k-NN method should be executed for object identification and categorization.

Environmental Engineering

1. Weather Prediction

• By means of employing k-NN, our team plans to forecast weather situations.

2. Air Quality Index Prediction

• As a means to forecast air quality indices, it is approachable to utilize the k-NN algorithm.

3. Energy Consumption Forecasting

• Through the utilization of k-NN, we focus on predicting energy utilization.

4. Water Quality Monitoring

• Typically, k-NN must be executed for tracking quality of water.

5. Wildlife Habitat Classification

• With the support of the k-NN method, our team intends to categorize wildlife habitations.

Sports Analytics

1. Player Performance Prediction

• By utilizing k-NN, we aim to forecast the effectiveness of a player in sports.

2. Team Formation Optimization

• In order to reinforce creation of teams, it is advisable to employ k-NN.

3. Injury Prediction

• With the support of k-NN, we forecast wounds in athletes.

4. Game Outcome Prediction

• Through the utilization of the k-NN method, our team intends to forecast the result of sports games.

5. Scouting and Recruitment

• In sport areas, we must acquire the benefit of KNN for enlistment and scouting purposes.

Others

1. Recommendation Systems

• For products or movies, our team plans to apply a k-NN-related recommendation model.

2. Customer Segmentation

• Specifically, for commercial activities, divide consumers by means of employing the k-NN algorithm.

3. Anomaly Detection

• In different datasets, focus on identifying abnormalities through the utilization of k-NN.

4. Handwritten Text Recognition

• With the aid of k-NN, it is appreciable to identify handwritten text.

5. Automated Essay Scoring

• In an automatic manner, score the essays with the help of k-NN method.

Instance Project: k-NN Classifier for Iris Dataset

The following is an extensive instance for a simple k-NN classifier through the utilization of the Iris dataset.

1. Load and Preprocess Data:

load fisheriris

X = meas;

Y = species;

cv = cvpartition(Y, ‘HoldOut’, 0.3);

X_train = X(training(cv), :);

Y_train = Y(training(cv), :);

X_test = X(test(cv), :);

Y_test = Y(test(cv), :);

2. Train k-NN Classifier:

k = 5;

knnModel = fitcknn(X_train, Y_train, ‘NumNeighbors’, k);

3. Predict and Evaluate:

Y_pred = predict(knnModel, X_test);

confMat = confusionmat(Y_test, Y_pred);

accuracy = sum(diag(confMat)) / sum(confMat(:));

disp([‘Accuracy: ‘, num2str(accuracy * 100), ‘%’]);

4. Visualize Results:

figure;

confusionchart(Y_test, Y_pred);

title(‘Confusion Matrix for k-NN Classification’);

Through this article, we have offered a procedural instruction to apply k-NN in MATLAB for classification as well as regression. Also, 50 project plans with concise outlines are suggested by us in an extensive manner.

Milestones