Lévy mutation in artificial bee colony algorithm for gasoline price prediction

In this paper, a mutation strategy that is based on Lévy Probabily Distribution is introduced in Artificial Bee Colony algorithm. The purpose is to better exploit promising solutions found by the bees.Such an approach is used to improve the performance of the original ABC in optimizing Least Squares Support Vector Machine hyper parameters.From the conducted experiment, the proposed lvABC shows encouraging results in optimizing parameters of interest.The proposed.lvABC-LSSVM has outperformed existing prediction model, Backpropogation Neural Network (BPNN), in predicting gasoline price

SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance

Machine Learning algorithms have been widely used to solve various kinds of data classification problems. Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicated and computationally expensive, especially when the number of possible different combinations of variables is so high. Support Vector Machine (SVM) has been proven to perform much better when dealing with high dimensional datasets and numerical features. Although SVM works well with default value, the performance of SVM can be improved significantly using parameter optimization. We applied two methods which are Grid Search and Genetic Algorithm (GA) to optimize the SVM parameters. Our experiment showed that SVM parameter optimization using grid search always finds near optimal parameter combination within the given ranges. However, grid search was very slow; therefore it was very reliable only in low dimensional datasets with few parameters. SVM parameter optimization using GA can be used to solve the problem of grid search. GA has proven to be more stable than grid search. Based on average running time on 9 datasets, GA was almost 16 times faster than grid search. Futhermore, the GA’s results were slighlty better than the grid search in 8 of 9 datasets

Meta-learning recommendation of default hyper-parameter values for SVMs in classifications tasks

Machine learning algorithms have been investigated in several scenarios, one of them is the data classification. The predictive performance of the models induced by these algorithms is usually strongly affected by the values used for their hyper-parameters. Different approaches to define these values have been proposed, like the use of default values and optimization techniques. Although default values can result in models with good predictive performance, different implementations of the same machine learning algorithms use different default values, leading to models with clearly different predictive performance for the same dataset. Optimization techniques have been used to search for hyper-parameter values able to maximize the predictive performance of induced models for a given dataset, but with the drawback of a high computational cost. A compromise is to use an optimization technique to search for values that are suitable for a wide spectrum of datasets. This paper investigates the use of meta-learning to recommend default values for the induction of Support Vector Machine models for a new classification dataset. We compare the default values suggested by the Weka and LibSVM tools with default values optimized by meta-heuristics on a large range of datasets. This study covers only classification task, but we believe that similar ideas could be used in other related tasks. According to the experimental results, meta-models can accurately predict whether tool suggested or optimized default values should be used.CAPESCNPqSão Paulo Research Foundation (FAPESP) (grant#2012/23114-9

Optimasi Parameter Support Vector Machine menggunakan Genetic Algorithm untuk Klasifikasi Microarray Data

Support Vector Machine (SVM) merupakan metode machine learning untukmengklasifikasikan data yang telah berhasil digunakan utuk menyelesaikan permasalahan dalam berbagai bidang. Prinsip risk minimization yang digunakan dapat menghasilkan model SVM dengan kemampuan generalisasi yang baik. Permasalahan yang terdapat dalam metode SVM adalah kesulitan dalam menentukan hyperparameter SVM yang optimal, padahal pengaturan nilai parameter secara tepat akan meningkatkan akurasi klasifikasi SVM. Penelitian ini menggunakan Genetic Algorithm (GA) untuk mengoptimasi hyperparameter SVM. Optimasi GA pada SVM dibandingkan dengan optimasi Grid Search untuk membentuk model SVM yang digunakan untuk mengklasifikasikan data pada data microarray, yatu Data Colon Cancer dan Data Leukemia. Dari hasilanalisis, metode GA-SVM dapat menghasilkan performa klasifikasi yang lebih baik dibandingkan metode Grid Search SVM untuk data Colon. Pada data Leukemia, metode GA-SVM menghasilkan performa klasifikasi yang sama dengan metode Grid Search SVM, yaitu 100% untuk masing masing ukuran performa klasifikasi. ========================================================================= Support Vector Machine (SVM) is a machine learning method to classify data that has been successfully used to solve problems in various fields. The principle of risk minimization that can be used to produce SVM model have good generalization capability. The problem in the SVM method is the difficulty in determining the optimal SVM hyperparameter, whereas setting the parameter values appropriately will improve the accuracy of SVM classification. This study uses Genetic Algorithm (GA) to optimize SVM hyperparameter. GA optimization in SVM compared with Grid Search optimization to form the SVM model use to classify data on microarray data, Colon Cancer dataset and Leukemia dataset. From the analysis result, GA-SVM method can yield better classification performance than Grid Search SVM for Colon data. In the Leukemia data, GA-SVM method resulted in the same classification performance with the Grid Search SVM method, which is 100% for each classification performance measure