Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Comparations of Supervised Machine Learning Techniques in Predicting the Classification of the Household’s Welfare Status

Poverty has been a major problem for most countries around the world, including Indonesia. One approach to eradicate poverty is through equitable distribution of social assistance for target households based on Integrated Database of social assistance. This study has compared several well-known supervised machine learning techniques, namely: Naïve Bayes Classifier, Support Vector Machines, K-Nearest Neighbor Classification, C4.5 Algorithm, and Random Forest Algorithm to predict household welfare status classification by using an Integrated Database as a study case. The main objective of this study was to choose the best-supervised machine learning approach in predicting the classification of household’s welfare status based on attributes in the Integrated Database. The results showed that the Random Forest Algorithm was the best

Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure

Machine Learning Algorithms to Classify Water Levels for Smart Irrigation Systems

Agriculture is the main source of food. With thepassing of time, there are dangers in order to preserve on thefreshwater in agriculture sector. Thus, one of solutions to savethe freshwater is enhancing the wastewater. Machine learning(ML) algorithms are used in several applications, such as smartirrigation, to reduce freshwater loss via building highperformance ML algorithms. This paper proposes fouralgorithms: support vector machine (SVM), decision tree (DT),SVM with Adaboost, and DT with Adaboost to classify waterlevels of sprinklers for smart irrigation. Here, five levels ofwater are classified– Max, High, Medium, Low, and Stop. Theproposed algorithms are tested to obtain which algorithmachieves better performance and higher accuracy. Five stepssequentially are implemented on the used dataset via Pandasand Scikit-learn frameworks. The steps are preprocessing data,feature selection, feature scaling, training, and classification; toanalyze the performance of the algorithms. The results showedthat the DT algorithm with Adaboost is the best algorithmcompared to the rest of the algorithms. The DT algorithmachieves an accuracy score of 0.912 with a shorter testing timeof 2.2 seconds and mean square error (MSE) of 0.08

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models

Machine Learning Algorithms to Classify Water Levels for Smart Irrigation Systems

Agriculture is the main source of food. With thepassing of time, there are dangers in order to preserve on thefreshwater in agriculture sector. Thus, one of solutions to savethe freshwater is enhancing the wastewater. Machine learning(ML) algorithms are used in several applications, such as smartirrigation, to reduce freshwater loss via building highperformance ML algorithms. This paper proposes fouralgorithms: support vector machine (SVM), decision tree (DT),SVM with Adaboost, and DT with Adaboost to classify waterlevels of sprinklers for smart irrigation. Here, five levels ofwater are classified– Max, High, Medium, Low, and Stop. Theproposed algorithms are tested to obtain which algorithmachieves better performance and higher accuracy. Five stepssequentially are implemented on the used dataset via Pandasand Scikit-learn frameworks. The steps are preprocessing data,feature selection, feature scaling, training, and classification; toanalyze the performance of the algorithms. The results showedthat the DT algorithm with Adaboost is the best algorithmcompared to the rest of the algorithms. The DT algorithmachieves an accuracy score of 0.912 with a shorter testing timeof 2.2 seconds and mean square error (MSE) of 0.08