Adaptive building of hybrid learning model for detecting and adapting concept drifting data streams. Issue 1 (9th December 2014)

• Record Type: Journal Article
• Authors: Patil, Pramod D.; Kulkarni, Parag
• Is Part Of: International journal of knowledge engineering and data mining. Volume 3:Issue 1(2014)
• Page Start: 58
• Page End: 81
• Publication Date: 2014-12-09
• Subjects: adaptive learning models -- decision tree -- data streams -- concept drift -- reinforcement learning -- electricity market deregulation -- electricity prices -- electricity demand -- concept clusters; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

Abstract

In recent developments in electricity market deregulation, the prices are not fixed. In such application, class labels are not available directly and potentially valuable information is lost. A learning model of electricity demand and prices needs to be adaptive for dynamic changes in massive data streams. This paper presents adaptive building of hybrid learning model for electricity prices by detecting and adapting concept drifting data streams. A proposed framework is build with four main challenges such as future assumption, change type, learner adaptivity and model. A decision tree is built incrementally using Q–learning and Hoeffding bounds. A k–mode method produces concept clusters to handle unlabelled data, change type in terms of deviations between past concept clusters and current ones and predicts future assumptions. An adaptive classify algorithm is developed for the predictive ability evaluation on the test set. Result of experiments using Elec2 data confirms applicability of methodology with more than 80% unlabelled data.