Rule-Based Classification

INFO

Technique in machine learning and data analysis where classification decisions are made based on predefined rules
Rules typically follow an “if-then” structure and are derived from training data, expert knowledge, or rule-mining algorithms

• Developed by: Rooted in expert systems and symbolic AI from the 1970s–1980s
• Core Principle: Uses explicit logical conditions to categorize data points
• Search Strategy:
  • Rules can be extracted from:
    • Decision trees
    • Association rule mining (e.g., Apriori algorithm)
    • Domain expert knowledge
  • Enables interpretable decision-making with transparent logic

Workflow

1. Rule Definition
   • Manually or algorithmically define classification rules
   • Use logical conditions based on feature thresholds
2. Rule Application
   • Apply rules to each data point
   • Assign class labels based on matched conditions

Code Example

import pandas as pd
 
# Sample dataset with customer transactions
data = {
    "Customer_ID": [101, 102, 103, 104, 105],
    "Total_Purchases": [15, 2, 8, 12, 1],
    "Total_Spend": [1200, 150, 800, 950, 50],
    "Last_Purchase_Days_Ago": [30, 210, 90, 45, 365]
}
df = pd.DataFrame(data)
 
# Define classification function
def classify_customer(row):
    if row["Total_Purchases"] > 10 and row["Total_Spend"] > 1000:
        return "Loyal Customer"
    elif row["Total_Purchases"] < 3 and row["Last_Purchase_Days_Ago"] > 180:
        return "At-Risk Customer"
    elif row["Total_Purchases"] >= 5 and row["Total_Spend"] > 500:
        return "Regular Customer"
    else:
        return "Occasional Customer"
 
# Apply classification
df["Customer_Category"] = df.apply(classify_customer, axis=1)
 
# Display the classified results
import ace_tools as tools
tools.display_dataframe_to_user(name="Rule-Based Classification Results", dataframe=df)

Advantages

• Interpretability
  • Decisions are based on explicitly defined rules
  • Easy to explain and audit
  • Valuable in regulatory environments
• Computationally efficient
  • No iterative training required
  • Fast execution on structured datasets

Disadvantages

• Rigid and inflexible
  • Rules must be manually defined
  • Requires frequent updates for dynamic data
• Limited in handling complex relationships
  • Struggles with non-linear interactions or high-dimensional data