Evaluation of Efficiency of Linear Techniques to Optimize Attribute Space in Machine Learning: Relevant Results for Extractive Methods of Summarizing
نویسندگان
چکیده
One major challenge in the field of machine learning, especially in classification problems, is to optimize the attribute space in order to obtain a classification function, which will be used to discriminate future items. Several approaches to optimize the attribute space can be used: some of them select the most relevant attributes and the other ones extract certain attributes to create a new smaller set of variables. These classification approaches have recently been implemented in the automatic summarization process with promising results. This paper enriches these first results with another new experiment. Five well-known linear methods were exploited to optimize the attribute space in an original manner on a corpus of 1250 text documents. These methods, used in data clustering and unsupervised machine learning, allow either attribute selection (Singular Value Decomposition, K-Means, Kohonen Neural Networks) or new attribute extraction (Principal Component Analysis, Factor Analysis). After having applied these methods to optimize attribute space, the validation phase was focused on the discrimination power of the obtained classification function. For that, six techniques of machine learning were used to abduce the classification function. Its performance was evaluated with the metric Fmesure and ROC curves. The results show that the application of the five chosen linear methods for optimizing attribute space in the automatic summarization process by extraction is relevant. They also show which machine learning technique is preferable to use with each linear method to obtain a better efficiency.
منابع مشابه
Machine learning algorithms in air quality modeling
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...
متن کاملBiogeography-Based Optimization Algorithm for Automatic Extractive Text Summarization
Given the increasing number of documents, sites, online sources, and the users’ desire to quickly access information, automatic textual summarization has caught the attention of many researchers in this field. Researchers have presented different methods for text summarization as well as a useful summary of those texts including relevant document sentences. This study select...
متن کاملApplication of ensemble learning techniques to model the atmospheric concentration of SO2
In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...
متن کاملFault 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 کاملA Hybrid Machine Learning Method for Intrusion Detection
Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implemen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Computer and Information Science
دوره 5 شماره
صفحات -
تاریخ انتشار 2012