An Overview of Inductive Learning Algorithms

Abstract

Inductive learning enables the system to recognize patterns and regularities in previous knowledge or training data and extract the general rules from them. In literature there are proposed two main categories of inductive learning methods and techniques. Divide-and-Conquer algorithms also called decision Tree algorithms and Separate-and-Conquer algorithms known as covering algorithms. This paper first briefly describe the concept of decision trees followed by a review of the well known existing decision tree algorithms including description of ID3, C4.5 and CART algorithms. A well known example of covering algorithms is RULe Extraction System (RULES) family. An up to date overview of RULES algorithms, and Rule Extractor-1 algorithm, their solidity as well as shortage are explained and discussed. Finally few application domains of inductive learning are presented.

Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique

“…RULE Extraction System has been separated from the several covering algorithms due to its simplicity. RULES-1 were the first member of the RULES family [15]. Second version of the RULES has been developed and applied variousdomains [16].…”

Categorization of Factors Affecting Classification Algorithms Selection

“…Some classification algorithms can be used with categorical and continuous predictors while others can be used with categorical only [41]. Also, some algorithms are more appropriate than others when it comes to the predicted field, or the target, as some are able to classify only categorical targets.…”

“…Handling missed data is a crucial subtask in data preprocessing phase [10,31,41,55]. That is one reason why the ratio of missing values is always importantly considered as a significant statistical metadata measure of the dataset and considered in many pieces of research [19,20,21,24,30,37].…”

“…Symbolic algorithms are simple, easy to implement, interpret, and represent a good compromise between simplicity and complexity [40,43,46]. A lot of studies carried out to describe, review and compare these algorithms [10,31,41,42,45]. On one hand, it was reported that symbolic algorithms are a good choice for maximizing classification accuracy if the metadata of the dataset shows that the data has extreme distribution [10,45].…”

Factors Affecting Classification Algorithms Recommendation: A Survey

“…Handling these missed values is a very important subtask in data preprocessing phase [10,31,41,55]. That is one reason, why the ratio of missing values is always importantly considered as a significant statistical metadata measure of the dataset and considered in many pieces of research [19,20,21,24,30,37].…”