Selection of features and combination of classifiers using a fuzzy approach for acoustic event classification
نویسندگان
چکیده
In this paper, we aim to improve the classification of human non-speech sounds produced in a meeting-room environment by using concepts and tools from the fuzzy theory. Starting with an SVM-based baseline system, firstly a reduction of the number of features with the fuzzy measure is shown. And, secondly, a noticeable improvement of the classification performance is reported by combining the outputs of two classification systems with the fuzzy integral.
منابع مشابه
A Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization
Spam is an unwanted email that is harmful to communications around the world. Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. Nonetheless, in spam detection, there are a large num...
متن کاملA Real-Time Electroencephalography Classification in Emotion Assessment Based on Synthetic Statistical-Frequency Feature Extraction and Feature Selection
Purpose: To assess three main emotions (happy, sad and calm) by various classifiers, using appropriate feature extraction and feature selection. Materials and Methods: In this study a combination of Power Spectral Density and a series of statistical features are proposed as statistical-frequency features. Next, a feature selection method from pattern recognition (PR) Tools is presented to e...
متن کاملEnsemble Classification and Extended Feature Selection for Credit Card Fraud Detection
Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...
متن کاملOptimal Feature Extraction for Discriminating Raman Spectra of Different Skin Samples using Statistical Methods and Genetic Algorithm
Introduction: Raman spectroscopy, that is a spectroscopic technique based on inelastic scattering of monochromatic light, can provide valuable information about molecular vibrations, so using this technique we can study molecular changes in a sample. Material and Methods: In this research, 153 Raman spectra obtained from normal and dried skin samples. Baseline and electrical noise were eliminat...
متن کاملAutomatic Sleep Stages Detection Based on EEG Signals Using Combination of Classifiers
Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005