Critic-driven Ensemble Classification via a Learning Method Akin to Boosting
نویسندگان
چکیده
منابع مشابه
Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods
Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...
متن کاملNew Ensemble Method for Classification of Data Streams
Classification of data streams has become an important area of data mining, as the number of applications facing these challenges increases. In this paper, we propose a new ensemble learning method for data stream classification in presence of concept drift. Our method is capable of detecting changes and adapting to new concepts which appears in the stream. Data stream classification; concept d...
متن کاملThe prediction of lymphedema via the combination of the selected data mining algorithms
Background: Breast cancer is the second leading cause of cancer death in women, after lung cancer. Due to the importance of predicting this disease, the use of data mining methods in medical research is more significant than before. Data mining algorithms can be a great help in preventing the development of lymphedema in patients. The aim Of this study was to create a diagnosis system that can ...
متن کاملCombining Feature Selection and Ensemble Learning for Software Quality Estimation
High dimensionality is a major problem that affects the quality of training datasets and therefore classification models. Feature selection is frequently used to deal with this problem. The goal of feature selection is to choose the most relevant and important attributes from the raw dataset. Another major challenge to building effective classification models from binary datasets is class imbal...
متن کاملFuzzy cognitive map ensemble learning paradigm to solve classification problems: Application to autism identification
Fuzzy cognitive maps have gained considerable research interest and widely used to analyze complex systems and making decisions. Recently they have been found large applicability in diverse domains for decision support and classification tasks. A new learning paradigm for FCMs is proposed in this research work, inheriting the main aspects of ensemble based learning approaches, such as bagging a...
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 2001