Integrating Data Selection and Extreme Learning Machine for Imbalanced Data

Mahdiyah, Umi; Irawan, Mohammad Isa; Imah, Elly Matul

doi:10.1016/j.procs.2015.07.561

Cited by 19 publications

(14 citation statements)

References 6 publications

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“…However, ELM is always applied for balanced data. Imbalanced data problems require special treatment because characteristics of the imbalanced data can decrease the accuracy of the data [33]. …”

Section: Extreme Learning Machinementioning

confidence: 99%

Research and Application of a Hybrid Wind Energy Forecasting System Based on Data Processing and an Optimized Extreme Learning Machine

Wang

et al. 2018

Energies

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Accurate wind speed forecasting plays a significant role for grid operators and the use of wind energy, which helps meet increasing energy needs and improve the energy structure. However, choosing an accurate forecasting system is a challenging task. Many studies have been carried out in recent years, but unfortunately, these studies ignore the importance of data preprocessing and the influence of numerous missing values, leading to poor forecasting performance. In this paper, a hybrid forecasting system based on data preprocessing and an Extreme Learning Machine optimized by the cuckoo algorithm is proposed, which can overcome the limitations of the single ELM model. In the system, the standard genetic algorithm is added to reduce the dimensions of the input and utilize the time series model for error correction by focusing on the optimized extreme learning machine model. And according to screened results, the 5% fractile and 95% fractile are applied to compose the upper and lower bounds of the confidence interval, respectively. The assessment results indicate that the hybrid system successfully overcomes some limitations of the single Extreme Learning Machine model and traditional BP and Mycielski models and can be an effective tool compared to traditional forecasting models.

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Section: Extreme Learning Machinementioning

confidence: 99%

Research and Application of a Hybrid Wind Energy Forecasting System Based on Data Processing and an Optimized Extreme Learning Machine

Wang

et al. 2018

Energies

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“…Each protein molecule will be linked to a specific cellular biochemical pathway that will only bind to certain ligand structures. The chemical signal of a ligand that binds to a protein molecule will cause a tissue response, which activates or inhibits the biochemical pathways associated with protein [2].…”

Section: Introductionmentioning

confidence: 99%

“…The functions of proteins include forming enzymes and hormones, forming blood cells, and making antibodies to protect the body from disease and infection [3]. The proteinligand binding site is a protein sac that binds or forms chemical bonds with other molecules and ions (ligands) [2]. The binding of proteins by binding sites is often reversible and can be stable or unstable depending on the structure and activity.…”

Section: Introductionmentioning

confidence: 99%

Predicting Protein-Ligand Binding Site for Drug Design Using Context Relevant Self Organizing Maps (CRSOM)

Imah¹,

Wulandari²

2020

IJIES

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Research on binding sites has been done to find suitable ligands to treat a particular disease. The binding site is a pocket on the surface of the protein, which acts as a place to attach a ligand. In bioinformatics, searching for binding sites is applied to drug design problems. Currently, computer-aided drug design has been developed. In this study, the prediction of protein-ligand binding sites formulated as a binary classification, which is distinguish the location that has potential to binding the ligand and the location that has no potential to binding the ligand. The dataset that will be used in this research is taken from the RCSB Protein Data Bank of 14 proteins data. The classification method used in this research is Context Relevant Self Organizing Maps (CRSOM), where the CRSOM method gives higher accuracy results compared to Backpropagation and Deep Learning. Context Relevant Self Organizing Maps (CRSOM) is chosen as a supervised learning classification algorithm that has an optimal internal representation, where data belonging different classes are separated with wider margin, while data belonging to the same class are clustered closely to each other. Thus, CRSOM is able to visualize high-dimensional protein data into binding site and non-binding site classes significantly. The results of the study obtained an average training accuracy of 99,60%, testing accuracy of 96.26%, and the average test time of 28.63 seconds, the result is better than the predecessor.

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“…2,4 While boosting is an example of an algorithmic approach that recalculates weights with each iteration to place different weights on the training examples. 5 High dimensionality is one of the obstacles facing the mining of clinical data because high dimensionality causes high computational costs, difficulties interpreting data and may influence the classification performance. The dimensionality reduction categories have two types; feature extraction and feature selection.…”

Section: Introductionmentioning

confidence: 99%

Resampling Imbalanced Class and the Effectiveness of Feature Selection Methods for Heart Failure Dataset

Khaldy

Kambhampati

2018

IRATJ

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The real dataset has many shortcomings that pose challenges to machine learning. High dimensional and imbalanced class prevalence is two important challenges. Hence, the classification of data is negatively impacted by imbalanced data, and high dimensional could create suboptimal performance of the classifier. In this paper, we explore and analyse different feature selection methods for a clinical dataset that suffers from high dimensional and imbalance data. The aim of this paper is to investigate the effect of imbalanced data on selecting features by implementing the feature selection methods to select a subset of the original data and then resample the dataset. In addition, we resample the dataset to apply feature selection methods on a balanced class to compare the results with the original data. Random forest and J48 techniques were used to evaluate the efficacy of samples. The experiments confirm that resampling imbalanced class obtains a significant increase in classification performance, for both taxonomy methods Random forest and J48. Furthermore, the biggest measure affected by balanced data is specificity where it is sharply increased for all methods. What is more, the subsets selected from the balanced data just improve the performance for information gain, where it is played down for the performance of others.

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Integrating Data Selection and Extreme Learning Machine for Imbalanced Data

Cited by 19 publications

References 6 publications

Research and Application of a Hybrid Wind Energy Forecasting System Based on Data Processing and an Optimized Extreme Learning Machine

Research and Application of a Hybrid Wind Energy Forecasting System Based on Data Processing and an Optimized Extreme Learning Machine

Predicting Protein-Ligand Binding Site for Drug Design Using Context Relevant Self Organizing Maps (CRSOM)

Resampling Imbalanced Class and the Effectiveness of Feature Selection Methods for Heart Failure Dataset

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