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A co-learning framework for learning user search intents from rule-generated training data

Jun Yan, Zeyu Zheng, Li Jiang, Yan Li, Shuicheng Yan, Zheng Chen
2010 Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10  
In this paper, we introduce a Co-learning Framework (CLF) to tackle the problem of learning from biased and noisy rule-generated training data.  ...  Motivated by this bottleneck, we start with some user common sense, i.e. a set of rules, to generate training data for learning to predict user intents.  ...  In this paper, we propose a co-learning framework (CLF) for learning user intent from the rule-generated training data, which are possibly biased and noisy.  ... 
doi:10.1145/1835449.1835670 dblp:conf/sigir/YanZJLYC10 fatcat:s7evu2mfufdwxlkderit5ewqwy

Hybrid ensemble learning approach for generation of classification rules

Han Liu, Alexander Gegov, Mihaela Cocea
2015 2015 International Conference on Machine Learning and Cybernetics (ICMLC)  
For example, inductive learning algorithms involve generation of rules which can be in the form of either a decision tree or if-then rules.  ...  Due to the daily increase in the size of data, machine learning has become a popular approach for intelligent processing of data.  ...  The competitions mentioned above aim to have each single rule with a quality as high as possible for each rule set generated from a sample of training data.  ... 
doi:10.1109/icmlc.2015.7340951 dblp:conf/icmlc/LiuGC15 fatcat:ude55vk6e5fg3mvxnlvwm77i6a

Integrating Explanatory and Descriptive Learning in ILP

Yannis Dimopoulos, Saso Dzeroski, Antonis C. Kakas
1997 International Joint Conference on Artificial Intelligence  
The two components allow us to combine complementary information from the same data by applying both explanatory and descriptive learning methods.  ...  We present a semantics for the new framework and then discuss different cases where combin ing information from explanatory and descrip tive ILP could be useful.  ...  The integrity constraint expresses a general regularity that characterizes the available data is also used to com pensate for the overgenerality of the learned rule in T.  ... 
dblp:conf/ijcai/DimopoulosDK97 fatcat:n7sa3v7czvflplluhl6tlwkzby

Data Programming by Demonstration: A Framework for Interactively Learning Labeling Functions [article]

Sara Evensen and Chang Ge and Dongjin Choi and Çağatay Demiralp
2020 arXiv   pre-print
Here we propose a new framework, data programming by demonstration (DPBD), to generate labeling rules using interactive demonstrations of users.  ...  We operationalize our framework with Ruler, an interactive system that synthesizes labeling rules for document classification by using span-level annotations of users on document examples.  ...  In summary, we contribute (1) DPBD, a general data independent framework for learning labeling rules by interactive demonstration; (2) RULER, an interactive system operationalizing our framework for document  ... 
arXiv:2009.01444v3 fatcat:5medghxvz5a3ri2xuiuvagxghi

NSL: Hybrid Interpretable Learning From Noisy Raw Data [article]

Daniel Cunnington, Alessandra Russo, Mark Law, Jorge Lobo, Lance Kaplan
2021 arXiv   pre-print
This paper introduces a hybrid neural-symbolic learning framework, called NSL, that learns interpretable rules from labelled unstructured data.  ...  NSL combines pre-trained neural networks for feature extraction with FastLAS, a state-of-the-art ILP system for rule learning under the answer set semantics.  ...  A differentiable ILP framework [30] called ∂ILP learns rules from unstructured data by reimplementing a top-down, generate and test ILP approach.  ... 
arXiv:2012.05023v2 fatcat:turskrvmizeq3heiaqrbsvjteu

Multiagent Inductive Learning: an Argumentation-based Approach

Santiago Ontañón, Enric Plaza
2010 International Conference on Machine Learning  
This paper focuses on concept learning, and presents A-MAIL, a framework for multiagent induction integrating ideas from inductive learning, case-based reasoning and argumentation.  ...  We also identify the requirements for learning algorithms to be used in our framework, and propose an algorithm which satisfies them.  ...  An Argumentation Framework for Inductive Learning This section presents A-MAIL, an Argumentation Framework for Inductive Learning for two agents; a more complex framework for n agents is beyond the scope  ... 
dblp:conf/icml/OntanonP10 fatcat:q6rj6itjuzhlfhn34k3a6voc34

A Paradigm-shifting from Domain-Driven Data Mining Frameworks to Process-based Domain-Driven Data Mining-Actionable Knowledge Discovery Framework

Fakeeha Fatima, Ramzan Talib, Muhammad Kashif Hanif, Muhammad Awais
2020 IEEE Access  
We observed that the inclusion of rule learning factors only from dataset or from domain knowledge is not sufficient.  ...  To improve rule actionability, different researchers have initially presented various Data Mining (DM) frameworks by focusing on different factors only from the business domain dataset.  ...  Still, there is a gap between D3M generated learned rules and achieving actual business goals due to missing the processcentric context while making erudite learned rules for taking actionable decisions  ... 
doi:10.1109/access.2020.3039111 fatcat:5anv6meb55drjjrl3ibsk3j644

Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS [article]

Mahardhika Pratama, Choiru Za'in, Eric Pardede
2018 arXiv   pre-print
Many distributed machine learning frameworks have recently been built to speed up the large-scale data learning process.  ...  To address this problem, we propose a novel Evolving Large-scale Data Stream Analytics framework based on a Scalable Parsimonious Network based on Fuzzy Inference System (Scalable PANFIS), where the PANFIS  ...  Distributed computing focuses on how to distribute/parallelize data processing from a single-node CPU-based processing into multi-node cluster-based processing framework [6] , thus accelerate the learning  ... 
arXiv:1807.06996v1 fatcat:7qahlwfccrhq3hohmpsjnupjkq

Big Data Analytic based on Scalable PANFIS for RFID Localization [article]

Choiru Za'in and Mahardhika Pratama and Andri Ashfahani and Eric Pardede and Huang Sheng
2018 arXiv   pre-print
However, the data used for localization task is not easy to analyze because it is generated from the non-stationary environment.  ...  We propose a distributed big data analytic framework based on PANFIS (Scalable PANFIS), where PANFIS is an evolving algorithm which has capability to learn data stream in the single pass mode.  ...  This research is also supported by use of the Nectar Research Cloud, a collaborative Australian research platform supported by the National Collaborative Research Infrastructure Strategy (NCRIS).  ... 
arXiv:1804.10166v1 fatcat:aixyxyilzfggdhh2xoezbmjd4a

Investigation of Software Defect Prediction Using Data Mining Framework

M. Anbu, G.S. Anandha Mala
2015 Research Journal of Applied Sciences Engineering and Technology  
The experimental results shows that, one pass algorithm generate rules for software defect prediction with consider amount of time and with better performance.  ...  The Software defect prediction is a method which predict defect from historical database. Data mining Techniques are used to predict Software defects from historical databases.  ...  From Fig. observe that (1) For MGF framework, the alance diff values are always positive except for the KC1 data. K SUBRAHMANYAM.  ... 
doi:10.19026/rjaset.11.1676 fatcat:z2g7txero5e2rjafwkblsrlehm

Associative learning based intrusion detection using sensor prioritization and fusion

2011 2011 Integrated Communications, Navigation, and Surveillance Conference Proceedings  
Dua, LA Tech 34 Nmap on DMRL; nmap.org Develop algorithm capable of learning from a given heterogeneous diverse Develop algorithm capable of learning from a given heterogeneous diverse data f f Dynamic  ...  Climactic Data Center (for 700 locations across US) Synthetic Data: Provided by a CRU weather generator that uses a Markov chain model to generate simulated weather data for 11 UK sites Associative  ... 
doi:10.1109/icnsurv.2011.5935373 fatcat:d75g3bxb7bghdin5774pi4uggm

Integrating Defeasible Argumentation and Machine Learning Techniques [article]

Sergio Alejandro Gomez, Carlos Ivan Chesñevar
2004 arXiv   pre-print
We suggest how different aspects of a generic argument-based framework can be integrated with other ML-based approaches.  ...  In this paper we outline different alternatives for combining defeasible argumentation and machine learning techniques.  ...  Default rules are generated as specializations of general rules that cover positive examples, whereas exceptions to general rules are identified from negative examples and are then generalized to rules  ... 
arXiv:cs/0402057v2 fatcat:gcxzssyoa5at3jbo2rgfxa4zqm

Building Domain Ontologies From Relational Database Using Mapping Rules

Maruf Pasha, Abdul Sattar
2012 International Journal of Intelligent Engineering and Systems  
), stored data (through data mining) and also performed a comparative analysis of these techniques.  ...  Therefore, we have focused on domain specific relational databases for constructing ontologies as a solution.  ...  The framework is a domain/application independent and can learn ontology for general or specific domains from relational database.  ... 
doi:10.22266/ijies2012.0331.03 fatcat:tdi5r5oq4fdupgmzinjpt54mki

Collaborative Decision Making by Ensemble Rule Based Classification Systems [chapter]

Han Liu, Alexander Gegov
2015 Studies in Big Data  
Rule based classification is a popular approach for decision making.  ...  It is also achievable that multiple rule based classifiers work together for group decision making by using ensemble learning approach.  ...  In the latter way of ensemble learning, the first algorithm learns a model from data and then the second algorithm learns to correct the former one etc [1] .  ... 
doi:10.1007/978-3-319-16829-6_10 fatcat:bau7zpjg4ng6xjymdymnmaybh4

Towards Model-informed Precision Dosing with Expert-in-the-loop Machine Learning [article]

Yihuang Kang, Yi-Wen Chiu, Ming-Yen Lin, Fang-yi Su, Sheng-Tai Huang
2021 arXiv   pre-print
With an application to precision dosing, our experimental results show that the approach can learn interpretable rules from data and may potentially lower experts' workload by replacing data annotation  ...  We propose a novel human-in-the-loop ML framework aimed at dealing with learning problems that the cost of data annotation is high and the lack of appropriate data to model the association between the  ...  We thus propose an interactive ML framework that promotes "model annotation" (as opposed to data annotation) by directly prompting uncertain labels or new data representations in rules learned from the  ... 
arXiv:2106.14384v2 fatcat:67yfjazsy5c5lohv3zyvsf5t2i
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