{"doi":"10.1109/ictai.2017.00039","title":"Evaluation of Transfer Learning Algorithms Using Different Base Learners","abstract":null,"journal":"2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)","year":2017,"id":588604,"datarank":0.524541517806853,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.23265499544855592,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.23265499544855592,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":6,"citers_with_citation_signal":5,"citers_with_endowment":5,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1505878,"name":"Taghi Khoshgoftaar","orcid":null,"position":1,"is_corresponding":false},{"id":1505877,"name":"Karl Weiss","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Evaluation of Transfer Learning Algorithms Using Different Base Learners","abstract":"In the field of supervised machine learning, a transfer learning environment is defined as the training data having different distribution characteristics than the testing data. This is due to the lack of available labeled data for the domain of interest, which prompts an alternate domain to be used as the training data. Because there is insufficient labeled data from the domain of interest, validation techniques cannot be reliably used for the algorithm selection process in a transfer learning environment. A transfer learning algorithm is typically comprised of a domain adaptation step followed by a learning step. The learning step is usually implemented using a traditional machine learning algorithm. In this paper, we examine and analyze the impact that the traditional machine learning algorithm (the learning step) has on the overall performance of a transfer learning algorithm. Using the transfer learning test framework, we test five state-of-the-art transfer learning algorithms coupled with seven different traditional learning algorithms for a total of 35 unique transfer learning algorithms. For our experiment, no labeled data from the domain of interest is available for the training process. Since validation techniques cannot be reliably used for the algorithm selection process in a transfer learning environment, it is important for machine learning researchers and practitioners to understand the impact of a traditional machine learner on the overall performance of a transfer learning algorithm.","is_dataset_classified":null,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26657633","pmcid":null,"openalex_id":"https://openalex.org/W2806899865","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2021,"count":2},{"year":2024,"count":2},{"year":2025,"count":2}],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/8344747/8371903/08371942.pdf?arnumber=8371942","host_type":"publisher"},{"url":"https://doi.org/10.1109/ictai.2017.00039","host_type":""}],"fields_of_study":["Domain Adaptation and Few-Shot Learning","Machine Learning and ELM","Data Stream Mining Techniques","Computer Science"],"mesh_terms":[],"keywords":["Machine learning","Computer science","Transfer of learning","Artificial intelligence","Algorithm","Instance-based learning","Online machine learning","Inductive transfer","Active learning (machine learning)","Semi-supervised learning","Computational learning theory","Process (computing)","Domain (mathematical analysis)","Robot learning","Wake-sleep algorithm","Learning classifier system","Stability (learning theory)","Mathematics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Quality Education"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-22T05:11:56.200148Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}