Decision tree Induction Research Papers (original) (raw)

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- Decision Tree, Information Gain, Decision tree Induction
- by Jae Kyeong Kim
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- Data Mining, Personalization, Data Warehousing, Intelligent Agent
- by Gary Koehler
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- Marketing, Inductive Learning, OMEGA, Business and Management
- by Marc Kaltenbach
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- Machine Learning, Distance Learning, Learning Style, Multi Agent System
- by Georgios Petasis
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- Machine Learning, Named Entity Recognition, Language Resources, Decision Tree
- by Uwe Zscherpel
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- Cognitive Science, Machine Learning, Neural Networks, Neural Network

... state control regulations. Unfortunately, other European waters have suffered massive oil pollution,ie the recent tanker breakups during stormy weather conditions of Erika in the ... particularly in bad weather conditions, ie ropes... more

- by Alexopoulos Aristotelis
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- Machine Learning, MARINE POLLUTION, Inductive Learning, Oil Spill
- by Francis Andoh-Baidoo
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- Data Mining, Internet Security, Mathematical Sciences, Regression
- by Ricardo Cerri
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- Data Mining, Support Vector Machines, Gene expression, Decision Trees
- by John Mingers
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- Cognitive Science, Machine Learning, Decision Trees, Uncertain Data
- by jabbar afsharkohan
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- Decision Making, Data Mining, Decision tree Induction
- by Kweku-Muata Osei-Bryson
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- Decision Making, Data Mining, Data Envelopment Analysis, Heterogeneity
- by Petra Perner
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- Cognitive Science, Machine Learning, Neural Networks, Neural Network

The paper discusses the use of decision trees for probability-based ranking. Emphasis is placed on ranking problems in question answering, where the frequency of correct candidates is very low but a single correct answer at one of the top ranks is often sufficient. Since existing tree learners handle this task poorly, decision tree induction is reformulated in such a way

- by Ingo Glöckner
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- Information Retrieval, A Priori Knowledge, Probability, Decision Trees
- by Armin Shmilovici
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- Machine Learning, Data Mining, Semiconductor Manufacturing, Neural Networks
- by Rafael Robina Ramírez
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- Decision Tree, Definite Clause Grammar, First Order Logic, Rule Extraction
- by Ran Wolff
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- Genetics, Distributed Database, Data Mining, Distributed Algorithms

This paper describes an application of CBR with decision tree induction in a manufacturing setting to analyze the cause for defects reoccurring in the domain. Abstraction of domain knowledge is made possible by integrating CBR with decision trees. The CID approach augments the recall and reuse done by CBR with statistical analysis that is focused towards the discovery of connections between specific defects and their possible causes. We show that this discovery also gives a pointer towards a corresponding corrective action.

- by radhika b
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- Machine Learning, Statistical Analysis, Case Based Reasoning, Decision Tree
- by Gail Jarvik
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- Engineering, Computer Science, Algorithms, Artificial Intelligence
- by Roni Romano
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- Data Mining, Comparative Study, Feature Selection, Intelligent Manufacturing
- by Floriana Esposito
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- Optimization Problem, State Space, Decision Tree, Prediction Accuracy
- by R. Gilleron
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- Machine Learning, Data Mining, Semi-supervised Learning, Learning problems
- by Kanishka Bhaduri
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- Data Mining, Distributed System, Decision Tree, Peer to Peer
- by Kanishka Bhaduri
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- Statistics, Data Mining, Distributed System, Decision Trees
- by Giovanni Semeraro
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- Decision Tree, Decision tree Induction
- by Sattar Hashemi
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- Decision Tree, Bit Error Rate, Learning Methods, Decision tree Induction
- by Jae Kyeong Kim
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- Information Systems, Personalization, Applied Economics, Commerce

Machine learning has been identified as a promising approach to knowledge-based system development. This study focused on the use of decision-tree induction for knowledge acquisition to filter individual-cow lactations for group-average lactation curve analysis. Data consisted of 1428 cases, classified by a dairy-nutrition specialist as outliers (34 cases) or non-outliers. The classification performance was estimated through 10-fold cross validation. A

- by kevin wade
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- Engineering, Machine Learning, Knowledge Based System, Knowledge Acquisition
- by Mohammad Kamrul Hossain
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- Gene expression, Classification, Key words, Decision Tree
- by Parvinder Sandhu
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- Machine Learning, Human Body, Decision Tree, Protein Function
- by Armin Shmilovici
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- Data Mining, Process Control, Process Engineering, Manufacturing Engineering

We present an analysis of partial automation of content analysis using machine learning methods. We use a decision-tree induction system to learn from manually categorized negotiation transcripts of electronic buyer–seller negotiations. The data we use were gathered using the Web-based negotiation support systems Inspire and SimpleNS. We experiment with various ways of representing the data to find the solution that gives the best results. The experiments show that we can identify, in relatively small data sets, linguistic features of interest for the detection of negotiation behaviour and negotiation-specific topics.

- by Stanisław Szpakowicz and +1
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- Machine Learning, Content Analysis, Business and Management, Negotiation support system
- by Milos Jovanovic
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- Computer Science, Design, Data Mining, Classification (Machine Learning)
- by Assaf Schuster
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- Computer Science, Distributed Database, Data Mining, Distributed Algorithms
- by Niki Kunene
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- Computer Science, Data Mining, Traumatic Brain Injury, A Priori Knowledge
- by Milos Jovanovic
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- Computer Science, Design, Data Mining, Classification (Machine Learning)

Knowledge Acquisition is an important task when developing image interpretation systems. Whereas in the past this task has been done by interviewing an expert, the current trend is to collect large data bases of images associated with... more

- by Petra Perner
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- X-ray imaging, Image Interpretation, Lung Cancer, Knowledge Acquisition
- by B Samanta
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- Engineering, Algorithms, Artificial Intelligence, Data Mining

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combin...

- by H. Hogeveen
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- Dairy Science, Animal Production, Decision Trees, Humans

Traditional Genetic Algorithms (GA) use crossover and mutation as the main genetic operators to achieve population diversity. Previous work using a biologically inspired genetic operator called transposition, allowed the GA to reach better solutions by replacing the traditional crossover operators. In this paper we extend that work to the case of asexual reproduction. The GA efficiency was compared when using

- by Ernesto COSTA
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- Genetics, Public Domain, Genetic Algorithm, Scaling up
- by Sunil Vadera
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- Public Health, Neural Network, Diabetes mellitus, Decision Support