Time-Series-Classification

This is a project for my machine learning course focusing on classification of time series. I aim to classify the activities of humans based on time series obtained by a Wireless Sensor Network.

Data Source:

https://archive.ics.uci.edu/ml/datasets/Activity+Recognition+system+based+on+Multisensor+data+fusion+\%28AReM\%29

Data Description:

• 7 folders that represent 7 types of activities
• in each folder, there are multiple files each of which represents an instant of a human performing an activity
• Each file contains 6 time series collected from activities of the same person
• 88 instances in the dataset, each of which containes 6 time series and each time series has 480 consecutive values

Tasks:

Feature Extraction

• Extract the time-domain features (min, max, mean, median, sd, q1, q3) for all of the 6 time series in each instances.
• Bootstrap confidence interval for the standard deviation of each feaature

Binary Classification: Classify bending from other activities using Logistic Regression

• Backward selection & Recursive Feature Elimination
• 5-fold cross-validation: report cross-validation accuracy
• Use stratified cross validation to handle class-imbalance
• Report confusion matrix, ROC, AUC, parameters, p-values
• Report accuracy on the test set

Binary Classification: Classify bending from other activities using Logistic Regression with case-control sampling to handle imabalanced clasees

• Report confusion matrix, ROC, AUC, parameters, p-values
• Report accuracy on the test set

Binary Classification: Classify bending from other activities using L1-penalized logistic regression

• Find optimal paraments through cross-validation
• Variable selection using p-values

Multi-class Classification: Classify all activities using L1-penalized multinomial regression model

• Find optimal parameters through cross-validation
• Report test error & accuracy
• Report confusion matrices

Multi-class Classification: Classify all activities using Gaussian Naive Bayes' Classifier

• Find optimal parameters through cross-validation
• Report test error & accuracy
• Report confusion matrices

Multi-class Classification: Classify all activities using Multinomial Naive Bayes' Classifier

• Find optimal parameters through cross-validation
• Report test error & accuracy
• Report confusion matrices