Skip to content

Algorithms Overview

SmartKNN extends classical nearest-neighbor learning into a structured, multi-stage system.

Rather than treating KNN as a single operation, SmartKNN decomposes nearest-neighbor inference into explicit algorithmic components, each responsible for a specific aspect of performance, robustness, and interpretability.

This page provides a high-level overview of those components and how they interact within the system.

High-Level Pipeline

At a high level, SmartKNN operates in two phases:

  1. Preparation and learning
  2. Inference and prediction

Each phase consists of multiple, explicitly defined steps that are modular, inspectable, and tunable.

Training-Time Components

During the preparation phase, SmartKNN analyzes the dataset to determine how similarity and prediction should behave at inference time.

Key steps include:

  1. Feature Weight Learning
    The system estimates the relative importance of each feature based on its contribution to prediction quality.

  2. Feature Pruning (Optional)
    Weak or noisy features may be down-weighted or excluded to improve stability and efficiency.

  3. Distance Configuration
    Learned weights and scaling factors are incorporated into the distance computation logic.

  4. Backend Preparation
    Based on dataset size and configuration, SmartKNN prepares the appropriate execution backend (e.g., brute-force or approximate nearest neighbors).

These steps define both how similarity is measured and how neighbors will be retrieved during inference.

Inference-Time Components

Once configured, SmartKNN performs prediction using a deterministic, stage-based inference pipeline:

  1. Query Preprocessing
    Input samples are normalized and transformed consistently with the training data.

  2. Neighbor Retrieval
    Candidate neighbors are retrieved using the selected backend strategy.

  3. Distance Computation
    Distances are computed using the configured distance engine and learned feature weights.

  4. Neighbor Selection
    The top-K nearest neighbors are selected based on weighted distance.

  5. Prediction Aggregation
    Neighbor contributions are combined to produce the final prediction, along with optional interpretability outputs.

Each step is explicitly defined to ensure predictable behavior and debuggability.

Core Algorithmic Components

SmartKNN is organized around the following core components:

  • Feature Weight Learning
    Determines how strongly each feature influences similarity.

  • Feature Pruning Engine
    Reduces dimensionality and noise by suppressing weak features.

  • Distance Engine
    Defines how similarity is computed between samples.

  • Backend Strategy
    Controls how neighbors are retrieved efficiently at different scales.

  • Prediction Logic
    Aggregates neighbor information into final outputs and explanations.

Each component is documented in detail in its respective section.

Component Interaction Model

SmartKNN components are designed to operate in a coordinated manner:

  • Feature weights directly influence distance computation.
  • Distance behavior affects backend efficiency.
  • Backend choice introduces latency and approximation trade-offs.
  • Prediction logic depends on both distance quality and neighbor selection.

This explicit interaction model enables controlled trade-offs between accuracy, performance, and interpretability.

Design Intent

The algorithmic structure of SmartKNN reflects several core design intentions:

  • Avoid monolithic or opaque behavior
  • Enable targeted optimization of individual components
  • Ensure deterministic and predictable inference given a configuration
  • Support future extensions without breaking core logic

SmartKNN treats nearest-neighbor learning as a system-level algorithm, not a single formula.

Next Steps

For deeper technical detail, continue with the following sections:

  • Feature Weight Learning
  • Feature Pruning Engine
  • Distance Engine
  • Backend Strategy
  • Prediction Logic

Each section builds on the concepts introduced here.