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Generating Classification Rules From Training Samples, Arun D. Kulkarni
Generating Classification Rules From Training Samples, Arun D. Kulkarni
Arun Kulkarni
In this paper, we describe an algorithm to extract classification rules from training samples using fuzzy membership functions. The algorithm includes steps for generating classification rules, eliminating duplicate and conflicting rules, and ranking extracted rules. We have developed software to implement the algorithm using MATLAB scripts. As an illustration, we have used the algorithm to classify pixels in two multispectral images representing areas in New Orleans and Alaska. For each scene, we randomly selected 10 per cent of the samples from our training set data for generating an optimized rule set and used the remaining 90 per cent of samples …
Knowledge Extraction From Metacognitive Reading Strategies Data Using Induction Trees, Christopher Taylor, Arun D. Kulkarni, Kouider Mokhtari
Knowledge Extraction From Metacognitive Reading Strategies Data Using Induction Trees, Christopher Taylor, Arun D. Kulkarni, Kouider Mokhtari
Kouider Mokhtari
The assessment of students’ metacognitive knowledge and skills about reading is critical in determining their ability to read academic texts and do so with comprehension. In this paper, we used induction trees to extract metacognitive knowledge about reading from a reading strategies dataset obtained from a group of 1636 undergraduate college students. Using a C4.5 algorithm, we constructed decision trees, which helped us classify participants into three groups based on their metacognitive strategy awareness levels consisting of global, problem-solving and support reading strategies. We extracted rules from these decision trees, and in order to evaluate accuracy of the extracted rules, …
Assessing Metacognitive Skills Using Adaptive Neural Networks, Anderson Justin, Kouider Mokhtari, Arun Kulkarni
Assessing Metacognitive Skills Using Adaptive Neural Networks, Anderson Justin, Kouider Mokhtari, Arun Kulkarni
Kouider Mokhtari
The assessment of student's levels of metacognitive knowledge and skills is critical in determining their ability to effectively perform complex cognitive tasks such as solving mathematics or reading comprehension problems. In this paper, we use an adaptive multiplayer perceptron model to categorize participants based on their metacognitive awareness and perceived use of reading strategies while reading. Eight hundred and sixty-five middle school students participated in the study. All participants completed a 30-item instrument- the Metacognitive Awareness-of-Reading Strategies Inventory (MARSI). We used adaptive multi-layer perceptron models to classify participants into three groups based on their metacognitive strategy awareness levels using thirteen …
Knowledge Extraction From Metacognitive Reading Strategies Data Using Induction Trees, Christopher Taylor, Arun D. Kulkarni, Kouider Mokhtari
Knowledge Extraction From Metacognitive Reading Strategies Data Using Induction Trees, Christopher Taylor, Arun D. Kulkarni, Kouider Mokhtari
Arun Kulkarni
The assessment of students’ metacognitive knowledge and skills about reading is critical in determining their ability to read academic texts and do so with comprehension. In this paper, we used induction trees to extract metacognitive knowledge about reading from a reading strategies dataset obtained from a group of 1636 undergraduate college students. Using a C4.5 algorithm, we constructed decision trees, which helped us classify participants into three groups based on their metacognitive strategy awareness levels consisting of global, problem-solving and support reading strategies. We extracted rules from these decision trees, and in order to evaluate accuracy of the extracted rules, …
Assessing Metacognitive Skills Using Adaptive Neural Networks, Anderson Justin, Kouider Mokhtari, Arun Kulkarni
Assessing Metacognitive Skills Using Adaptive Neural Networks, Anderson Justin, Kouider Mokhtari, Arun Kulkarni
Arun Kulkarni
The assessment of student's levels of metacognitive knowledge and skills is critical in determining their ability to effectively perform complex cognitive tasks such as solving mathematics or reading comprehension problems. In this paper, we use an adaptive multiplayer perceptron model to categorize participants based on their metacognitive awareness and perceived use of reading strategies while reading. Eight hundred and sixty-five middle school students participated in the study. All participants completed a 30-item instrument- the Metacognitive Awareness-of-Reading Strategies Inventory (MARSI). We used adaptive multi-layer perceptron models to classify participants into three groups based on their metacognitive strategy awareness levels using thirteen …
Knowledge Extraction From Survey Data Using Neural Networks, Khan Imran, Arun Kulkarni
Knowledge Extraction From Survey Data Using Neural Networks, Khan Imran, Arun Kulkarni
Arun Kulkarni
Surveys are an important tool for researchers. It is increasingly important to develop powerful means for analyzing such data and to extract knowledge that could help in decision-making. Survey attributes are typically discrete data measured on a Likert scale. The process of classification becomes complex if the number of survey attributes is large. Another major issue in Likert-Scale data is the uniqueness of tuples. A large number of unique tuples may result in a large number of patterns. The main focus of this paper is to propose an efficient knowledge extraction method that can extract knowledge in terms of rules. …
Multispectral Image Analysis Using Random Forest, Barrett Lowe, Arun Kulkarni
Multispectral Image Analysis Using Random Forest, Barrett Lowe, Arun Kulkarni
Arun Kulkarni
Classical methods for classification of pixels in multispectral images include supervised classifiers such as the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin 2001 for classification and clustering. Random Forest grows many decision trees for classification. To classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a classification. The forest chooses the classification having the most votes. Random …
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Zhongmei Yao
Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …
Sentiment Classification Through Semantic Orientation Using Sentiwordnet, Dr. Muhammad Zubair Asghar, Dr, Auranzeb Khan
Sentiment Classification Through Semantic Orientation Using Sentiwordnet, Dr. Muhammad Zubair Asghar, Dr, Auranzeb Khan
Dr. Muhammad Zubair Asghar
Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease in making choices regards to online shopping, choosing events, products, entities. In this paper, a rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual …
A Wrapper-Based Feature Selection For Analysis Of Large Data Sets, Jinsong Leng, Craig Valli, Leisa Armstrong
A Wrapper-Based Feature Selection For Analysis Of Large Data Sets, Jinsong Leng, Craig Valli, Leisa Armstrong
Leisa Armstrong
Knowledge discovery from large data sets using classic data mining techniques has been proved to be difficult due to large size in both dimension and samples. In real applications, data sets often consist of many noisy, redundant, and irrelevant features, resulting in degrading the classification accuracy and increasing the complexity exponentially. Due to the inherent nature, the analysis of the quality of data sets is difficult and very limited approaches about this issue can be found in the literature. This paper presents a novel method to investigate the quality and structure of data sets, i.e., how to analyze whether there …