ITS-632 Intro To Data Mining - Dr. Patrick Haney, Dept. Of I ✓ Solved

ITS-632 Intro to Data Mining Dr. Patrick Haney Dept. of Info

ITS-632 Intro to Data Mining Dr. Patrick Haney Dept. of Information Technology & School of Computer and Information Sciences University of the Cumberlands Chapter 5 Assignment: Classification: Alternative Techniques. 1) Define and provide an example of Rule Coverage and Accuracy. 2) What are the Characteristics of Rule-Based Classifiers? 3) What are the steps to building a rule set using a direct method such as RIPPER? 4) Describe what is used to separate data in Support Vector Machines. 5) List and describe the two types of classifiers used in Ensemble Methods.

Paper For Above Instructions

Introduction

This paper answers the five questions in the assignment on alternative classification techniques in data mining. The explanations synthesize standard definitions, practical examples, and algorithmic steps drawn from canonical texts and foundational papers in machine learning and data mining (Han et al., 2011; Mitchell, 1997). Each section is self-contained and written to be clear for readers familiar with classification tasks.

1. Rule Coverage and Rule Accuracy: Definitions and Example

Rule coverage and rule accuracy are two common metrics used to evaluate individual association or classification rules in rule-based systems (Witten et al., 2011). Rule coverage (also called support in some contexts) measures the proportion (or count) of data instances to which the rule antecedent applies. Formally, coverage(rule) = |{instances satisfying antecedent}| / N, where N is total instances. Rule accuracy (often called confidence or precision) measures the proportion of those covered instances for which the rule's consequent (class label) is correct: accuracy(rule) = |{instances satisfying antecedent and consequent}| / |{instances satisfying antecedent}|.

Example: Consider a dataset of 10,000 loan applications. A rule reads: "If income > $80k and credit_score > 700 then approve_loan = yes." Suppose 800 applications meet the antecedent, and among those 800, 720 were actually approved in the training labels. Then coverage = 800 / 10,000 = 8%, and accuracy = 720 / 800 = 90% (Han et al., 2011). High coverage with moderate accuracy or high accuracy with low coverage each have trade-offs depending on business goals.

2. Characteristics of Rule-Based Classifiers

Rule-based classifiers have distinct properties that make them appealing in many applications (Witten et al., 2011; Tan et al., 2005):

  • Interpretability: They produce human-readable if-then rules that can be easily understood and audited by domain experts.
  • Locality: Each rule focuses on a subregion of the input space; multiple rules together cover the full domain.
  • Modularity: Rules can often be added, removed, or modified without retraining the entire model.
  • Transparency and explainability: Decisions trace back to explicit antecedents and consequents, aiding compliance and debugging.
  • Flexibility with mixed data: They can handle nominal and ordinal attributes and easily incorporate domain knowledge.
  • Vulnerability to overfitting: If rules are overly specific, they may overfit noise; pruning and validation are therefore important (Mitchell, 1997).

3. Building a Rule Set with a Direct Method: RIPPER

RIPPER (Repeated Incremental Pruning to Produce Error Reduction) is a direct rule-learning algorithm developed by William Cohen (1995) that constructs rule sets in a greedy, iterative fashion. The high-level steps are:

  1. Sort classes by increasing prevalence (learn rules for the minority class first).
  2. For the current class, induce rules one at a time: grow a rule by greedily adding conditions that maximize some heuristic (e.g., information gain or reduced error), starting from the most general rule.
  3. Prune the grown rule using a pruning set or heuristics to reduce overfitting (remove conditions that do not reduce error on a validation set).
  4. Add the pruned rule to the rule set and remove the covered positive examples from the training set.
  5. Repeat grow-and-prune until no positive examples remain for the current class or stopping criteria are met.
  6. Post-process the rule set with optimization passes that attempt to replace, revise, or remove rules to reduce overall error (RIPPER’s optimization stage), often using information from both training and pruning sets (Cohen, 1995).
  7. Proceed to the next class and repeat until rules for all classes are learned.

RIPPER is efficient and designed to produce compact, accurate rule sets by combining greedy growing with validation-based pruning and post-hoc optimization (Cohen, 1995; Witten et al., 2011).

4. What Separates Data in Support Vector Machines

Support Vector Machines (SVMs) separate data by a decision hyperplane (linear or in a transformed feature space) that maximizes the margin between classes. The margin is the distance between the hyperplane and the nearest data points from each class (these nearest points are called support vectors) (Cortes & Vapnik, 1995). For linearly separable data, SVM finds the hyperplane w·x + b = 0 maximizing margin subject to correct classification constraints. For nonlinearly separable data, SVMs use kernel functions (e.g., radial basis function, polynomial) to implicitly map data into a higher-dimensional feature space where a linear separator with maximal margin can be found (Cortes & Vapnik, 1995). The optimization problem is convex, giving a unique global solution in standard formulations, and the sparse representation (only support vectors matter) enhances computational and predictive properties (Vapnik, 1995).

5. Ensemble Methods: Two Classifier Types

Ensemble methods combine multiple base classifiers to improve predictive performance and robustness. Two primary ensemble paradigms are bagging (bootstrap aggregating) and boosting (Breiman, 1996; Freund & Schapire, 1997):

  • Bagging (Bootstrap Aggregating): Bagging builds multiple classifiers independently on bootstrap-resampled versions of the training set and aggregates their predictions by majority vote (for classification) or averaging (for regression). Bagging reduces variance and is especially effective with high-variance base learners such as decision trees. Random Forests extend bagging by adding random feature selection during tree building, further decorrelating trees (Breiman, 1996).
  • Boosting: Boosting sequentially trains base learners, where each learner focuses on instances misclassified by previous learners. The final prediction is a weighted combination of the learners. AdaBoost is a seminal boosting algorithm that adjusts instance weights and aggregates weak learners into a strong composite classifier, often reducing both bias and variance and frequently achieving state-of-the-art performance (Freund & Schapire, 1997).

Both paradigms have theoretical grounding and practical success; choice depends on data characteristics and base learner behavior (Rokach & Maimon, 2005; Dietterich, 2000).

Conclusion

Rule coverage and accuracy quantify how widely and how correctly a rule applies. Rule-based classifiers are interpretable, local, and modular but require care against overfitting. RIPPER constructs rules via grow-and-prune cycles with optimization, producing compact rule sets. SVMs separate classes by maximal-margin hyperplanes in possibly transformed feature spaces using kernels. Finally, ensemble methods—bagging and boosting—combine multiple classifiers to reduce error through variance reduction or sequential error correction, respectively. These techniques form a complementary toolkit for constructing robust classifiers in practical data mining applications (Han et al., 2011; Witten et al., 2011).

References

  • Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. (Han et al., 2011)
  • Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. (Mitchell, 1997)
  • Cohen, W. W. (1995). Fast Effective Rule Induction. In Proceedings of the 12th International Conference on Machine Learning. (Cohen, 1995)
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. (Cortes & Vapnik, 1995)
  • Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. (Breiman, 1996)
  • Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. (Freund & Schapire, 1997)
  • Rokach, L., & Maimon, O. (2005). Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2), 1–39. (Rokach & Maimon, 2005)
  • Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.). Morgan Kaufmann. (Witten et al., 2011)
  • Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining. Addison-Wesley. (Tan et al., 2005)
  • Kotsiantis, S. B. (2007). Supervised Machine Learning: A Review of Classification Techniques. Informatica, 31, 249–268. (Kotsiantis, 2007)

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