Estimating Null Values in Database Using CBR and Supervised Learning Classification
نویسنده
چکیده
Database and database systems have been used widely in almost, all life activities. Sometimes missed data items are discovered as missed or null values in the database tables. The presented paper proposes a design for a supervised learning system to estimate missed values found in the university database. The values of estimated data items or data it items used in estimation are numeric and not computed. The system performs data classification based on Case-Based Reasoning (CBR) to estimate loosed marks of students. A data set is used in training the system under the supervision of an expert. After training the system to classify and estimate null values under expert supervision, it starts classification and estimation of null data by itself. Keywords—DataBase(DB);Data mining; Case-Based Reasoning (CBR); Classification;Null Values; Supervised Learning
منابع مشابه
Semi-Supervised Learning Based Prediction of Musculoskeletal Disorder Risk
This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...
متن کاملPhysical Activity Identification using Supervised Machine Learning and based on Pulse Rate
Physical activity is one of the key components for elderly in order to be actively ageing. Pulse rate is a convenient physiological parameter to identify elderly’s physical activity since it increases with activity and decreases with rest. However, analysis and classification of pulse rate is often difficult due to personal variation during activity. This paper proposed a CaseBased Reasoning (C...
متن کاملSupervised Classification of Texture Patterns with Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) is an efficient tool for clustering and supervised classification of various objects, including text document, musical recordings, gene expressions, and images. In this paper, we are concerned with supervised classification of texture patterns. NMF is used for creating localized nonnegative feature vectors and low-dimensional nonnegative encoding vectors f...
متن کاملReinforcement of Local Pattern Cases for Playing Tetris
In the paper, we investigate the use of reinforcement learning in CBR for estimating and managing a legacy case base for playing the game of Tetris. Each case corresponds to a local pattern describing the relative height of a subset of columns where pieces could be placed. We evaluate these patterns through reinforcement learning to determine if significant performance improvement can be observ...
متن کاملEstimating Null Values In Relational Database Systems Based On Genetic Algorithms
In this paper, we present a new method to estimate null values in relational database systems based on genetic algorithms. It can tune the membership functions of the linguistic values of the attributes in relational database systems for estimating null values. The proposed method can get a higher average estimated accuracy rate than the existing methods for estimating null values in relational...
متن کامل