---
title: "Sequential covering for rule induction"
subject: "Data Mining"
chapter: 3
key: "semester_7/data_mining:sequential-covering"
source: https://www.bctengineer.com/q/semester_7/data_mining/sequential-covering
site: BCT Engineering
---

# Sequential covering for rule induction

Data Mining · Chapter 3: Classification

- Asked in: year not given
- Video: [Machine Learning | Sequential Covering Algorithm](https://www.youtube.com/watch?v=wgyvJCGOSj8) — RANJI RAJ
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/sequential-covering
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-3

## Question

Explain Sequential covering algorithm for Rule Induction. What are the characteristics of Rule Based Classifier?

## Short answer

Sequential covering learns one rule at a time per class: it grows a rule by greedily adding the best attribute test, removes the covered records, and repeats until stopping criteria. Rule-based classifiers can be mutually exclusive and exhaustive with a default rule, are interpretable, and can handle imbalanced classes well.

## Answer

**Sequential covering** is a direct method of rule induction: it learns one rule at a time, removes the training records covered by that rule, and repeats on the remaining records until no more good rules can be found. It is the basis of AQ, CN2, RIPPER and FOIL.

#### Algorithm
```text
R = { }                         (rule set)
repeat for each class c:
    while stopping condition not met:
        r = Learn-One-Rule(Training set, c)
        remove records covered by r from the training set
        add r to R
Add a default rule (majority class) and return R
```

#### Learn-One-Rule (rule growing)
Start with the empty rule (IF TRUE THEN class c) and add the attribute test that best improves rule quality (e.g. FOIL's information gain or accuracy), until the rule is accurate enough. A rule is then pruned if needed. Rules are added until the stopping condition is met, such as no records left or the quality of a new rule falling below a threshold.

```text
All records ----> rule R1 covers some --> remove
Rest of records -> rule R2 covers some --> remove
Rest ...                                  --> default rule
```

#### Characteristics of a rule-based classifier
- **Mutually exclusive rules**: a record triggers at most one rule. If not, conflict resolution (ordering) is used.
- **Exhaustive rules**: every record is covered by some rule; otherwise a default rule is used.
- Rules can be **ordered** (decision list) or unordered (voting).
- Expressiveness is about equal to a decision tree, and rules are easy to interpret and quick to apply to new records.
- Can handle imbalanced classes, and can be built without building the tree first.

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. What does sequential covering do after learning a rule?
   A. Removes the attribute used in the rule
   B. Removes the training records covered by that rule and repeats
   C. Rebuilds the whole rule set from scratch
   D. Merges all rules into a tree
   Answer: B. It learns one rule at a time and removes covered records. (Key point: Core idea)

2. How does Learn-One-Rule start growing a rule?
   A. From the empty rule (IF TRUE THEN class c), adding the best test each step
   B. From the most specific rule covering one record only
   C. From a random full-length rule
   D. From the rule with the lowest coverage
   Answer: A. The empty rule is refined by adding the test that most improves quality. (Key point: Learn-One-Rule)

3. What is the purpose of the default rule at the end?
   A. To resolve conflicts between two rules
   B. To prune the first rule
   C. To classify records not covered by any rule, using the majority class
   D. To compute rule accuracy
   Answer: C. It makes the rule set exhaustive. (Key point: Default rule)

4. What does 'mutually exclusive' mean for a rule-based classifier?
   A. Every record triggers some rule
   B. Rules use different classes
   C. Rules are ordered by accuracy
   D. A record triggers at most one rule
   Answer: D. Mutual exclusivity means no record fires more than one rule; otherwise conflict resolution is needed. (Key point: Characteristics)

## Related in Data Mining

- [Rule based classifier; coverage and accuracy](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Arule-based-classifier-measures) — Data Mining, chapter 3
- [Rule based classifier pros and cons](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Arule-based-pros-cons) — Data Mining, chapter 3
- [Decision tree classifier and Hunt's algorithm](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adecision-tree-hunt) — Data Mining, chapter 3

## Related in other subjects

- [Rule based reasoning; backward chaining](https://www.bctengineer.com/question.md?key=semester_6%2Fartificial_intelligence%3Arule-based-reasoning-backward-chaining) — Artificial Intelligence, chapter 4
- [IDMT characteristics and induction disc relay](https://www.bctengineer.com/question.md?key=semester_6%2Fswitchgear_and_protection%3Aidmt-induction-disc-relay) — Switchgear and Protection, chapter 7, asked 2×