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Diabetes Mellitus is a dreadful disease characterized by increased levels of glucose in the blood, termed as the condition of hyperglycemia. As this disease is prominent among the tropical countries like India, an intense research is being carried out to deliver a machine learning model that could learn from previous patient records in order to deliver smart diagnosis. This research work aims to improve...
At present, most of the EEG emotion recognition studies have taken all electric shocks or filtered electrodes as a feature and they are integrated (combined) with simple features that are extracted from other signals as a single classifier Emotional classification, but there are problems such as low efficiency and low accuracy. Aiming at this problem, this paper proposes an EEG emotion classification...
the class imbalance is a major problem in machine learning. This problem affects the performance of a model prediction. The DBSM algorithm, a hybrid-sampling technique, was developed to deal with the class imbalance for two-class classification problem. Although the DBSM algorithm is the effective solution, there are too many parameters for tuning in the algorithm. Thus, this paper proposes an automatic...
Occupational accident is a serious issue for every industry. Steel industry is considered to be one of the economic sectors having a high number of accidents. Thus, the main aim of this study is to build a model which could predict the occupational incidents (i.e., injury, near-miss, and property damage) using support vector machine (SVM) by utilizing a database comprising almost 5000 occupational...
The feature subset selection, along with the parameters of classifier significantly influences the classification accuracy. In order to ensure the optimal classification performance, the artificial bee colony (ABC) algorithm is proposed to simultaneously optimize the feature subset and the parameters of support vector machines (SVM), meanwhile for improving the optimizing performance of ABC algorithm,...
Traditional classification algorithms addressing imbalanced-class dataset mostly concentrate on the majority classes' accuracy, such that the minority class's accuracy is usually ignored. Focusing on this issue, we propose a novel classification algorithm using Ensemble Feature Selections (EFS) for imbalanced-class dataset. This algorithm utilizes the superiority of EFS in accuracy, then considers...
Methods currently used for micro-array data classification aim to select a minimum subset of features, namely a predictor, that is necessary to construct a classifier of best accuracy. Although effective, they lack in facing the primary goal of domain experts that are interested in detecting different groups of biologically relevant markers. In this paper, we present and test a framework which aims...
Recently, it has been shown that, in ensemble learning, it may be preferable to ensemble some instead of all the classifiers. Various selective ensemble approaches are then designed, where optimization algorithms like genetic algorithm (GA) are used to evolve weights of component classifiers and classifiers with weights greater than a threshold are selected. This paper proposes a novel selective ensemble...
Ensemble learning is a method to improve the performance of classification and prediction algorithms. It has received considerable attention because of its prominent generalization and performance improvement. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes genetic algorithm-based coverage...
In this paper, four data-driven classification approaches, that is, K-nearest neighbors (K-NN), self-organizing map (SOM), multi-layer perceptron (MLP), and Bayesian Network classifier (BNC), are applied to a health condition monitoring problem for the wearing cutter. The dataset is produced from a cutting machine using force sensing. A genetic algorithm (GA) based search is performed to select 3...
Cost-sensitive classification algorithms that enable effective prediction, where the costs of misclassification can be very different, are crucial to creditors and auditors in credit risk analysis. Learning vector quantization (LVQ) is a powerful tool to solve bankruptcy prediction problem as a classification task. The genetic algorithm (GA) is applied widely in conjunction with artificial intelligent...
Microarray technology today has the ability of having the whole genome spotted on a single chip. It allows the biologist to inspect thousands of gene activities simultaneously. Machine learning approaches are suited and used to discovering the complex relationships between genes under controlled experimental conditions and classify microarray data by identifying a subset of informative genes embedded...
Data mining has been the active area of research in the last decade. The classification is one of the important task of data mining. Different kind of classification algorithms have been suggested and tested to predict the future events based on unseen data. The objective of this paper is to compare various classification models that have been frequently used in data mining. Three decision trees;...
Online auctions have become extremely popular in recent years. Ability to predict winning bid prices accurately can help bidders to maximize their profit. This paper proposes a number of strategies and algorithms for performing such predictions for the first price sealed bid reverse auctions (FPSBRA). The neural networks (NN) and genetic programming (GP) learning techniques are used in the models...
Data mining has as goal to extract knowledge from large databases. To extract this knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, which make it is infeasible to search exhaustively. As a search strategy, genetic algorithms have been applied successfully in many fields...
Aiming at solving the problem that there were large amounts of ineffective antibodies and the antibodies were lack of diversity in the traditional negative selection algorithm, this paper designed intrusion detection algorithm of artificial immune based on decision tree and genetic algorithm. The decision tree and the genetic algorithm were introduced into the traditional negative selection algorithm,...
In this paper, SVM based feature selection methods are introduced for regression problem of COX2 inhibitor activity prediction in Chinese medicine quantitative structure-activity relationship (QSAR) research. We develop a recursive SVM feature selection algorithm for regression and compare its performance with genetic algorithm and SVM recursive feature elimination (SVM-RFE) algorithm. Experiments...
This paper makes some improvements on MFCC feature extraction and proposes a quick MFCC algorithm which is used for Real-Time Speaker Recognition System. Based on the quick MFCC algorithm, the paper uses Differential MFCC for feature extraction and Vector Quantization plus GMM model for classification to achieve a better result. It can meet the requirements of real-time system in case of the high...
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