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Learn-One-Rule Algorithm
  • Last Updated : 26 Nov, 2020

Prerequisite: Rule-Based Classifier 

Learn-One-Rule: 

This method is used in the sequential learning algorithm for learning the rules. It returns a single rule that covers at least some examples (as shown in Fig 1). However, what makes it really powerful is its ability to create relations among the attributes given, hence covering a larger hypothesis space.  

For example:
IF Mother(y, x) and Female(y), THEN Daughter(x, y). 
Here, any person can be associated with the variables x and y

Fig 1: Learn-One-Rule Example

Learn-One-Rule Algorithm

The Learn-One-Rule algorithm follows a greedy searching paradigm where it searches for the rules with high accuracy but its coverage is very low. It classifies all the positive examples for a particular instance. It returns a single rule that covers some examples.



Learn-One-Rule(target_attribute, attributes, examples, k):

    Pos = positive examples
    Neg = negative examples
      best-hypothesis = the most general hypothesis
      candidate-hypothesis = {best-hypothesis}
      
      while candidate-hypothesis:     
      //Generate the next more specific candidate-hypothesis

            constraints_list = all contraints in the form "attribute=value"
            new-candidate-hypothesis = all specializations of candidate-
                           hypothesis by adding all-constraints
            remove all duplicates/inconsistent hypothesis from new-candidate-hypothesis. 
            //Update best-hypothesis
            best_hypothesis = argmax(h∈CHs) Performance(h,examples,target_attribute)
            
            //Update candidate-hypothesis

            candidate-hypothesis = the k best from new-candidate-hypothesis 
                           according to Performance.
      prediction = most frequent value of target_attribute from examples that match best-hypothesis
      IF best_hypothesis:
          return prediction 

It involves a PERFORMANCE method that calculates the performance of each candidate hypothesis. (i.e. how well the hypothesis matches the given set of examples in the training data.

Performance(NewRule,h):
    h-examples = the set of rules that match h
      return (h-examples)

It starts with the most general rule precondition, then greedily adds the variable that most improves performance measured over the training examples. 

Learn-One-Rule Example

Let us understand the working of the algorithm using an example: 

DayWeatherTempWindRainPlayBadminton
D1SunnyHotWeakHeavyNo
D2SunnyHotStrongHeavyNo
D3OvercastHotWeakHeavyNo
D4SnowyColdWeakLightYes
D5SnowyColdWeakLightYes
D6SnowyColdStrongLightYes
D7OvercastMildStrongHeavyNo
D8SunnyHotWeakLightYes
Step 1 - best_hypothesis = IF h THEN PlayBadminton(x) = Yes
Step 2 - candidate-hypothesis = {best-hypothesis}
Step 3 - constraints_list = {Weather(x)=Sunny, Temp(x)=Hot, Wind(x)=Weak, ......}
Step 4 - new-candidate-hypothesis = {IF Weather=Sunny THEN PlayBadminton=YES, 
                      IF Weather=Overcast THEN PlayBadminton=YES, ...}
Step 5 - best-hypothesis = IF Weather=Sunny THEN PlayBadminton=YES 
Step 6 - candidate-hypothesis = {IF Weather=Sunny THEN PlayBadminton=YES, 
                 IF Weather=Sunny THEN PlayBadminton=YES...} 
Step 7 - Go to Step 2 and keep doing it till the best-hypothesis is obtained.

You can refer to Fig 1. for a better understanding of how the best-hypothesis is obtained. [Step 5 & 6]

Sequential Learning Algorithm uses this algorithm, improving on it and increasing the coverage of the hypothesis space. It can be modified to accept an argument that specifies the target value of interest. 

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