Many pattern classification problems require a solution that needs to be incrementally updated over a period of time. Incremental learning problems are often complicated by the appearance of new concept classes and unbalanced cardinality in training data. The purpose of this research is to develop an algorithm capable of incrementally learning from severely unbalanced data. This work introduces three novel ensemble based algorithms derived from the incremental learning algorithm, Learn++. Learn++.NC is designed specifically for incrementally learning New Classes through dynamically adjusting the combination weights of the classifiers' decisions. Learn++.UD handles Unbalanced Data through class-conditional voting weights that are proportional to the cardinality differences among training datasets. Finally, we introduce the Boosted Ensemble Algorithm Strategically Trained (BEAST) for incremental learning of unbalanced data. BEAST combines Learn++.NC and Learn++.UD with additional strategies that compensate for unbalanced data arising from cardinality differences among concept classes. These three algorithms are investigated both analytically and empirically through a series of simulations. The simulation results are presented, compared and discussed. While Learn++.NC and Learn++.UD perform well on the specific problems they were designed for, BEAST provides a strong and more robust performance on a much broader spectrum of complex incremental learning and unbalanced data problems.
","abstract_html":"<p>Many pattern classification problems require a solution that needs to be incrementally updated over a period of time. Incremental learning problems are often complicated by the appearance of new concept classes and unbalanced cardinality in training data. The purpose of this research is to develop an algorithm capable of incrementally learning from severely unbalanced data. This work introduces three novel ensemble based algorithms derived from the incremental learning algorithm, Learn++. Learn++.NC is designed specifically for incrementally learning <em>N</em>ew <em>C</em>lasses through dynamically adjusting the combination weights of the classifiers' decisions. Learn++.UD handles <em>U</em>nbalanced <em>D</em>ata through class-conditional voting weights that are proportional to the cardinality differences among <em>training datasets</em>. Finally, we introduce the <em>B</em>oosted <em>E</em>nsemble <em>A</em>lgorithm <em>S</em>trategically <em>T</em>rained (BEAST) for incremental learning of unbalanced data. BEAST combines Learn++.NC and Learn++.UD with additional strategies that compensate for unbalanced data arising from cardinality differences among concept classes. These three algorithms are investigated both analytically and empirically through a series of simulations. The simulation results are presented, compared and discussed. While Learn++.NC and Learn++.UD perform well on the specific problems they were designed for, BEAST provides a strong and more robust performance on a much broader spectrum of complex incremental learning and unbalanced data problems.</p>","abstract_has_math":false,"creators":["Muhlbaier, Michael David"],"institution":null,"degree_name":"M.S. in Engineering","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Polikar, Robi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006-12-31T08:00:00Z","date_published":"2006-12-31T08:00:00Z","updated_at":"2026-07-24T04:13:46Z","subjects":["Electrical engineering--Research; Learning--Mathematical models","Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://rdw.rowan.edu/etd/915","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Polikar, Robi"]},{"key":"dc:creator","label":"Author","values":["Muhlbaier, Michael David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2016-04-06T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. in Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical engineering--Research; Learning--Mathematical models","Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://rdw.rowan.edu/etd/915"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Many pattern classification problems require a solution that needs to be incrementally updated over a period of time. Incremental learning problems are often complicated by the appearance of new concept classes and unbalanced cardinality in training data. The purpose of this research is to develop an algorithm capable of incrementally learning from severely unbalanced data. This work introduces three novel ensemble based algorithms derived from the incremental learning algorithm, Learn++. Learn++.NC is designed specifically for incrementally learning New Classes through dynamically adjusting the combination weights of the classifiers' decisions. Learn++.UD handles Unbalanced Data through class-conditional voting weights that are proportional to the cardinality differences among training datasets. Finally, we introduce the Boosted Ensemble Algorithm Strategically Trained (BEAST) for incremental learning of unbalanced data. BEAST combines Learn++.NC and Learn++.UD with additional strategies that compensate for unbalanced data arising from cardinality differences among concept classes. These three algorithms are investigated both analytically and empirically through a series of simulations. The simulation results are presented, compared and discussed. While Learn++.NC and Learn++.UD perform well on the specific problems they were designed for, BEAST provides a strong and more robust performance on a much broader spectrum of complex incremental learning and unbalanced data problems.
"]},{"key":"dc:title","label":"Title","values":["Boosted ensemble algorithm strategically trained for the incremental learning of unbalanced data"]}]}],"canonical_facts":{"dc:contributor":["Polikar, Robi"],"dc:creator":["Muhlbaier, Michael David"],"dc:date.available":["2016-04-06T07:00:00Z"],"dc:description.abstract":["Many pattern classification problems require a solution that needs to be incrementally updated over a period of time. Incremental learning problems are often complicated by the appearance of new concept classes and unbalanced cardinality in training data. The purpose of this research is to develop an algorithm capable of incrementally learning from severely unbalanced data. This work introduces three novel ensemble based algorithms derived from the incremental learning algorithm, Learn++. Learn++.NC is designed specifically for incrementally learning New Classes through dynamically adjusting the combination weights of the classifiers' decisions. Learn++.UD handles Unbalanced Data through class-conditional voting weights that are proportional to the cardinality differences among training datasets. Finally, we introduce the Boosted Ensemble Algorithm Strategically Trained (BEAST) for incremental learning of unbalanced data. BEAST combines Learn++.NC and Learn++.UD with additional strategies that compensate for unbalanced data arising from cardinality differences among concept classes. These three algorithms are investigated both analytically and empirically through a series of simulations. The simulation results are presented, compared and discussed. While Learn++.NC and Learn++.UD perform well on the specific problems they were designed for, BEAST provides a strong and more robust performance on a much broader spectrum of complex incremental learning and unbalanced data problems.
"],"dc:identifier":["https://rdw.rowan.edu/etd/915"],"dc:subject":["Electrical engineering--Research; Learning--Mathematical models","Electrical and Computer Engineering"],"dc:title":["Boosted ensemble algorithm strategically trained for the incremental learning of unbalanced data"],"thesis:degree_discipline":["Electrical & Computer Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S. in Engineering"]},"updated_at":"2026-07-24T04:13:46Z"}