Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO

Support Vector Machine and Its Difficulties From Control Field of View

The application of the Support Vector Machine (SVM) classification algorithm to large-scale datasets is limited due to its use of a large number of support vectors and dependency of its performance on its kernel parameter. In this paper, SVM is redefined as a control system and Iterative Learning Control (ILC) method is used to optimize SVM’s kernel parameter. The ILC technique first defines an error equation and then iteratively updates the kernel function and its regularization parameter using the training error and the previous state of the system. The closed-loop structure of the proposed algorithm increases the robustness of the technique to uncertainty and improves its convergence speed. Experimental results were generated using nine standard benchmark datasets covering a wide range of applications. Experimental results show that the proposed method generates superior or very competitive results in term of accuracy than those of classical and stateof-the-art SVM-based techniques while using a significantly smaller number of support vectors

Analysis of Support Vector Machine Regression for Building Energy Use Prediction

There are many inverse modeling methods to model the whole building energy use. Multiple linear regression (MLR) and change-point liner regression (CPLR) have been some of the most common methods due to their direct interpretation concerning building energy modeling and their fair accuracy. Recently, as machine-learning techniques have become more accessible, there have been many attempts to apply these techniques to building energy modeling. However, no studies have conducted an in-depth comparison with the conventional inverse model methods using large buildings sample size. This study conducted a comprehensive comparative study based on Support Vector Machine (SVM), one of the most widely used machine-learning methods for flexibility and accuracy, with enough cases to draw a reasonable conclusion between models generated from conventional methods such as MLR and CPLR, and those from SVM. This work, besides the comparative analysis, included a thorough SVM performance analysis for building energy modeling. It described in detail its implementation, and showed its performance as a regression technique for building energy modeling under the influence of different variables. The comparative study focused on modeling whole building chilled water use (CHW) and heating hot water use (HHW), and analyzed the influence of such variables as the outdoor dry-bulb temperature (OAT), the outdoor dew-point temperature (DPT), the outdoor air enthalpy (OAE), and the operational effective enthalpy (OEE). The numerical experiments were based on a sample of 41 whole year daily and hourly building energy use datasets that were converted from hourly data. According to the comparative analysis between SVM and MLR, based on CHW data, SVM consistently showed higher performances by an average of 6.8% on daily and 2.0% on monthly models, respectively. For the SVM and CPLR performance analysis, four pairs of dependent and independent variables were considered: CHW-OAT, CHW- OAE, CHW-OEE, and HHW-OAT. On daily modeling, SVM demonstrated consistently higher performance, although most of the cases resulted in a marginal advantage by less than 1% for all variables utilized. Despite such marginal gains in mean performance, SVM showed advantages by up to 3% for some datasets. On the monthly model, however, SVM did not exhibit better results for any dependent-independent variable pair

Analysis of Support Vector Machine Regression for Building Energy Use Prediction

There are many inverse modeling methods to model the whole building energy use. Multiple linear regression (MLR) and change-point liner regression (CPLR) have been some of the most common methods due to their intelligibility concerning building energy modeling and accuracy. Recently, as machine-learning techniques have become user-friendly, there have been an incremental number of attempts to apply these techniques to building energy modeling. However, few studies conducted an in-depth comparison with the conventional inverse model methods using large sample size. This study conducted an exhaustive comparative study based on Support Vector Machine (SVM), one of the most widely used machine-learning methods for flexibility and accuracy, with enough samples to draw a reasonable conclusion between models generated from conventional methods such as MLR and CPLR, and those from SVM. This work, besides the comparative analysis, included a thorough SVM performance analysis for building energy modeling. It described in detail its implementation, and showed its performance as a regression technique for building energy modeling under the influence of different variables. The comparative study focused on modeling whole building chilled water use (CHW) and heating hot water use (HHW), and analyzed the influence of such variables as the outdoor dry-bulb temperature (OAT), the outdoor dew-point temperature (DPT), the outdoor air enthalpy (OAE), and operational effective enthalpy (OEE). The numerical experiments were based on 41 whole year hourly building energy use dataset samples. These datasets were transformed into daily and monthly datasets. According to the comparative analysis between SVM and MLR, based on CHW datasets, SVM consistently showed higher performances by an average of 6.8% on daily and 2.0% on monthly models, respectively. For the SVM and CPLR performance analysis, four pairs of dependent and independent variables were considered: CHW-OAT, CHW-OAE, CHW-OEE, and HHW-OAT. On the daily model, SVM demonstrated consistently higher performances although most of the cases resulted in a marginal advantage by less than 1% for all variables utilized. Despite such marginal gains in mean performance, SVM showed advantages by up to 3% for some datasets. On the monthly model, however, SVM did not exhibit better results for any dependent-independent variable pair