Comparative Analysis Using k-Nearest Neighbors (KNN)

In text classification tasks, k-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machines (SVM) are three commonly used machine learning algorithms. Each has distinct advantages and disadvantages, making them suitable for different scenarios.

KNN (k-Nearest Neighbors) is a distance-based classification method that determines categories by calculating the distance between unclassified samples and their nearest neighbors in the training set. KNN's advantages include simplicity and intuitiveness with no training process required, but it suffers from high computational complexity, particularly underperforming on large-scale datasets. Implementation typically involves using distance metrics like Euclidean or cosine distance and scikit-learn's KNeighborsClassifier with configurable k-values.

Naive Bayes (NB) operates on Bayes' theorem with the assumption of feature independence. It performs excellently in text classification, especially for short texts like news categorization and spam filtering, offering fast computation and stable performance. However, its "naive" assumption may ignore feature correlations, leading to some accuracy loss. Common implementations use scikit-learn's MultinomialNB with Laplace smoothing for handling zero-frequency issues.

SVM (Support Vector Machine) finds optimal hyperplanes to maximize classification margins, making it suitable for high-dimensional data like TF-IDF text features. SVM performs exceptionally well on small-sample datasets but requires longer training times, with kernel selection significantly impacting results. Popular implementations involve scikit-learn's SVC class with kernel functions like linear, RBF, or polynomial requiring careful parameter tuning.

Experimental reports typically compare metrics like accuracy, recall, and F1-score across these algorithms while discussing how different features (bag-of-words, TF-IDF) affect performance. Cross-validation for parameter optimization (K-value for KNN, kernel functions for SVM) can further enhance classification effectiveness. Code implementations often employ scikit-learn's GridSearchCV for automated hyperparameter tuning.