Dynamic Aggregation Of Relational Attributes Research Articles

A Random Length Feature Construction Method for Learning Relational Data using DARA

In learning relational data, DARA (Dynamic Aggregation of Relational Attributes) algorithm transforms a relational data model representation into a vector space model representation. This data transformation is required in order to summarize or cluster data stored in relational databases in which a target record stored in a target table has a one-to-many relationship with non-target records stored in a non-target table. The descriptive accuracy of the summarized data performed by DARA is highly influenced by the representation of records stored in non-target tables that are associated with records stored in target table. This is important because when this summarized data is fed as input data for the classification task, the predictive accuracy of the classification task will also be affected. This paper proposes novel feature construction methods, called Variable Length Feature Construction without Substitution (VLFCWOS) and Variable Length Feature Construction with Substitution (VLFCWS), in order to construct a set of relevant features in learning relational data. These methods are proposed to improve the descriptive accuracy of the summarized data. In the process of summarizing relational data, a genetic algorithm is also applied and several feature scoring measures are evaluated in order to find the best set of relevant constructed features. In this work, we empirically compare the predictive accuracies of classification tasks based on the proposed feature construction methods and also the existing feature construction methods. The experimental results show that the predictive accuracy of classifying data that are summarized based on VLFCWS method using Total Cluster Entropy combined with Information Gain (CE-IG) as feature scoring outperforms in most cases

FEATURE TRANSFORMATION: A GENETIC‐BASED FEATURE CONSTRUCTION METHOD FOR DATA SUMMARIZATION

The importance of input representation has been recognized already in machine learning. This article discusses the application of genetic‐based feature construction methods to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Here, feature construction methods are applied to improve the descriptive accuracy of the DARA algorithm. The DARA algorithm is designed to summarize data stored in the nontarget tables by clustering them into groups, where multiple records stored in nontarget tables correspond to a single record stored in a target table. This article addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic‐based algorithm. This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features.

FEATURE TRANSFORMATION: A GENETIC‐BASED FEATURE CONSTRUCTION METHOD FOR DATA SUMMARIZATION

The importance of input representation has been recognized already in machine learning. This article discusses the application of genetic‐based feature construction methods to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Here, feature construction methods are applied to improve the descriptive accuracy of the DARA algorithm. The DARA algorithm is designed to summarize data stored in the nontarget tables by clustering them into groups, where multiple records stored in nontarget tables correspond to a single record stored in a target table. This article addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic‐based algorithm. This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features.

Optimizing Feature Construction Process for Dynamic Aggregation of Relational Attributes

Problem statement: The importance of input representation has been recognized already in machine learning. Feature construction is one of the methods used to generate relevant features for learning data. This study addressed the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. In other words, this paper discusses the application of genetic algorithm to optimize the feature construction process to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Approach: The DARA algorithm was designed to summarize data stored in the non-target tables by clustering them into groups, where multiple records stored in non-target tables correspond to a single record stored in a target table. Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm. Since, the study addressed the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process, the involved task includes solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic-based algorithm. Results: It is shown in the experimental results that the quality of summarized data is directly influenced by the methods used to create patterns that represent records in the (n×p) TF-IDF weighted frequency matrix. The results of the evaluation of the genetic-based feature construction algorithm showed that the data summarization results can be improved by constructing features by using the Cluster Entropy (CE) genetic-based feature construction algorithm. Conclusion: This study showed that the data summarization results can be improved by constructing features by using the cluster entropy genetic-based feature construction algorithm.