The Informative Vector Selection in Active Learning using Divisive Analysis
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 10
Abstract
Traditional supervised machine learning techniques require training on large volumes of data to acquire efficiency and accuracy. As opposed to traditional systems Active Learning systems minimizes the size of training data significantly because the selection of the data is done based on a strong mathematical model. This helps in achieving the same accuracy levels of the results as baseline techniques but with a considerably small training dataset. In this paper, the active learning approach has been implemented with a modification into the traditional system of active learning with version space algorithm. The version space concept is replaced with the divisive analysis (DIANA) algorithm and the core idea is to pre-cluster the instances before distributing them into training and testing data. The results obtained by our system have justified our reasoning that pre-clustering instead of the traditional version space algorithm can bring a good impact on the accuracy of the overall system’s classification. Two types of data have been tested, the binary class and multi-class. The proposed system worked well on the multi-class but in case of binary, the version space algorithm results were more accurate.
Authors and Affiliations
Zareen Sharf, Maryam Razzak
Keywords
Related Articles
- EP ID EP260750
- DOI 10.14569/IJACSA.2017.081009
- Views 106
- Downloads 0
An Advanced Emergency Warning Message Scheme based on Vehicles Speed and Traffic Densities
In intelligent transportation systems, broadcasting Warning Messages (WMs) by Vehicular Ad hoc Networks (VANETs) communication is a significant task. Designing efficient dissemination schemes for fast and reliable delive...
Using Multiple Seasonal Holt-Winters Exponential Smoothing to Predict Cloud Resource Provisioning
Elasticity is one of the key features of cloud computing that attracts many SaaS providers to minimize their services’ cost. Cost is minimized by automatically provision and release computational resources depend on actu...
Performance Comparison between MAI and Noise Constrained LMS Algorithm for MIMO CDMA DFE and Linear Equalizers
This paper presents a performance comparison between a constrained least mean squared algorithm for MIMO CDMA decision feedback equalizer and linear equalizer. Both algorithms are constrained on the length of spreading s...
Establishing Standard Rules for Choosing Best KPIs for an E-Commerce Business based on Google Analytics and Machine Learning Technique
The predictable values that indicate the performance of any company and determine that how well they are performing in order to achieve their objective is referred by the term called as “key performance indicators”. The...
Fuzzy C-Means based Inference Mechanism for Association Rule Mining: A Clinical Data Mining Approach
Association rule mining has wide variety of research in the field of data mining, many of association rule mining approaches are well investigated in literature, but the major issue with ARM is, huge number of frequent p...