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This paper focuses on accuracy improvement of human activities detection and classification by using single Inertia Measurement Unit sensor (IMU sensor: an acceleration sensor, a gyro sensor, a magnetometer, and an air pressure sensor) which is a type of the wearable sensors. Generally, performance of classification model is determined by these methodologies; number and type of sensors, coordinate...
In the context of the educational quality evaluation measured through standardized tests, this article aims to select the context variables that have a greater contribution in the differentiation of the categories of the 2015 SIMCE math score, for eighth grade students of the region of La Araucanía, Chile. Based on a cross-sectional research, a supervised classification design was implemented, defining...
The complexity of multidimensionality is one of the frequently encountered problems in the high-dimensional data space. The fact that multidimensionality in the data space increases and reaches great numbers brings about the problem that the number of non-informative ones among the features associated with the target class increases along with the dataset complexity. The fact that all features included...
Detecting diseases associated SNPs is the central goal of genetics and molecular biology. However, highthroughput techniques often provide long lists of disease SNPs candidates, and the identification of disease SNPs among the candidates set remains timeconsuming and expensive. In addition, contrasting to the number of SNPs involved, the available datasets (samples) generally have fairly small sample...
In the process of large-scale chemical engineering, more useful industrial process information can be obtained by increasing measuring variables, defined as system features. However, the increase in amount of features will lead to the high computation cost and reduce the efficiency of the process monitoring system. To solve this issue, those features that are redundant or bring an incorrect result...
Spam E-mailis a kind of electronic spam in which unsolicited messages are sent by E-mail. It is themost severe problem world-wide for decades. One of the best approach to identify spam E-mails is filtering E-mails by classification. In many applications feature selection isthe most widely used and essential task in many classification techniques to reduce the dimensionality of feature space. In this...
The breast cancer is one of the most popular cause of death among women. It is also one of the diseases that can be cured and has high healing chances when it is detected in the early stages [1]. Detecting the cancer and differentiating between the diagnosis that affirm whether a patient has breast cancer or not has been considered as a big challenge. In order to have an accurate diagnosis, Support...
Many disorders can be diagnosed by analysis of gene expression microarrays and this can save lots of lives. However, as gene expression data have high dimensions, establishing a method to identify the genes related to the target disease still remains a challenge, because it should provide a well-grounded prediction about the disease status. To this end, the best subset of genes should be distinguished...
Activity recognition has received a lot of attention from research scholars in the past few years. There has been a huge demand for activity recognition because of its ability to ease human-machine interaction, help in care for the elderly, and monitor the habitat requirements of the wildlife. In this paper, a Support Vector Machine (SVM) classifier to recognize the human activities has been built...
Forecasting electricity price allows market participants to make informed and sound decisions. Selecting the best training variables is often involved in forecasting in order to obtain optimal prediction. Support Vector Regression (SVR) provides an effective method to fit data and find minimal risk slack variables around a fit line. The best fit depends on the selected input feature set and the tuning...
In this paper, we propose a feature selection and representation combination method to generate discriminative features for speech emotion recognition. In feature selection stage, a Multiple Kernel Learning (MKL) based strategy is used to obtain the optimal feature subset. Specifically, features selected at least n times among 10-fold cross validation are collected to build a new feature subset named...
Next-generation sequencing allows high-throughput measurements of non-coding RNA expression levels in tissues. Analysis of microRNAs (miRNAs) is particularly effective in differentiation of cancerous tissue samples, based on patterns of their expression levels. The paper presents a wrapper feature selection approach based on t-Distributed Stochastic Neighbor Embedding (t-SNE), Covariance Matrix Adaptation...
The Beck Depression Inventory (BDI), a self-report questionnaire consisting of 21 question items, has been the most extensively used for depression assessment. The problem of interest here is to identify a subset of questions in the BDI that are most predictive of depression and can reveal gender differences between depression profiles. We investigate feature selection techniques to select a subset...
The disease Leukemia are continuously increasing among the people. The cause of leukemia is unknown but several factors, however are associated with the development of leukemia that are exposure to ionising radiation, exposure to benzene in rubber industry workers, cytotoxic drug particularly alkylating agent exposure, genetic disorder like down syndrome and immunological deficiency states. There...
Feature selection is an important step in many Machine Learning classification problems. It reduces the dimensionality of the feature space by removing noisy, irrelevant and redundant data, such that classification accuracy is enhanced while computational time remains affordable. In this paper, we present a new wrapper feature subset selection model based on Skewed Variable Neighborhood Search (SVNS)...
The explosive growth of webpage number on the Web has brought up some problems in the search process. One of these problems is that the general purpose search engines often return too many irrelevant results when users are searching for specific information on a given topic. Another problem is the massive increase in the number of pages to be indexed by Web search systems. In this research, two steps...
Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification...
Jamu is an Indonesia herbal medicine made from natural materials such as roots, leaves, fruits, and animals. The purpose of this research is to develop a classification system for jamu efficacy based on the composition of plants using Support Vector Machine (SVM) and to implement the k-means clustering algorithm as a feature selection method. The result of this study was compared to the previous research...
The paper presents the findings of an industry-based study in the utility of text categorization. The purpose of the study is to explore new approach to evaluate service quality of customer complaint handling. The industrial research setting is a large China insurance company. The text categorization methodologies are used in this research including nature language processing and machine learning...
The following paper proposes a set of novel feature selection criteria that can be applied to kernel Principal Component Analysis (kPCA) outcome to derive discriminative feature spaces for complex classification problems, such as biometric recognition tasks. The proposed class-separation criteria that are used to evaluate distributions of samples, which are projected onto nonlinear most discriminative...
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