BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230721T110302Z UID:E5AA44F5-D044-4815-9199-2EF00E4E1999 DTSTART;TZID=Asia/Kolkata:20230603T190000 DTEND;TZID=Asia/Kolkata:20230603T200000 DESCRIPTION:High-dimensional data can be challenging to work with because t he number of variables can make it difficult to visualize\, explore\, and analyze the data. Additionally\, high-dimensional datasets can be more sus ceptible to overfitting\, where a model performs well on the training data but poorly on new data\, due to the large number of variables. Techniques such as feature selection\, dimensionality reduction\, and regularization can help address these challenges. Meta-heuristic based feature selection is a technique used to select the most relevant features from a high-dime nsional dataset. Meta-heuristics are optimization algorithms that are desi gned to find near-optimal solutions to complex problems. Meta-heuristic ba sed feature selection has several advantages. It can handle high-dimension al datasets and can effectively select the most relevant features while re ducing overfitting. Additionally\, meta-heuristic algorithms are flexible and can be easily adapted to different types of models and fitness functio ns. Here we will discuss how to extract the optimal set of features from m eta-heuristic based feature selection approaches in high dimensional datas ets.\n\nSpeaker(s): Dr. Pradip Dhal\, \n\nVirtual: https://events.vtools.i eee.org/m/363094 LOCATION:Virtual: https://events.vtools.ieee.org/m/363094 ORGANIZER:ieee.cs.sbc.iit.kgp@gmail.com SEQUENCE:17 SUMMARY:A talk on the how to extract optimal set of features by applying me taheuristic-based feature selection approaches in high dimensional data URL;VALUE=URI:https://events.vtools.ieee.org/m/363094 X-ALT-DESC:Description: <br /><p><span style="font-weight: 400\;">High-dime nsional data can be challenging to work with because the number of variabl es can make it difficult to visualize\, explore\, and analyze the data. Ad ditionally\, high-dimensional datasets can be more susceptible to overfitt ing\, where a model performs well on the training data but poorly on new d ata\, due to the large number of variables. Techniques such as feature sel ection\, dimensionality reduction\, and regularization can help address th ese challenges. Meta-heuristic based feature selection is a technique used to select the most relevant features from a high-dimensional dataset. Met a-heuristics are optimization algorithms that are designed to find near-op timal solutions to complex problems. Meta-heuristic based feature selectio n has several advantages. It can handle high-dimensional datasets and can effectively select the most relevant features while reducing overfitting. Additionally\, meta-heuristic algorithms are flexible and can be easily ad apted to different types of models and fitness functions. Here we will dis cuss how to extract the optimal set of features from meta-heuristic based feature selection approaches in high dimensional datasets.</span></p> END:VEVENT END:VCALENDAR